Improving predictions of coastal benthic invertebrate occurrence and density using a multi-scalar approach. (1st November 2022)

• Record Type: Journal Article
• Authors: Watson, Stephanie L.; Stephenson, Fabrice; Pilditch, Conrad A.; Lundquist, Carolyn J.
• Is Part Of: Ocean & coastal management. Volume 230(2022)
• Publication Date: 2022-11-01
• Subjects: Marine resources -- Management -- Periodicals; Coastal zone management -- Periodicals; Coastal ecology -- Periodicals; Ressources marines -- Périodiques; Littoral -- Aménagement -- Périodiques; Écologie littorale -- Périodiques; Coastal ecology; Coastal zone management; Marine resources -- Management; Periodicals; Electronic journals; 551.46
• DOI: 10.1016/j.ocecoaman.2022.106355
• Languages: English
• ISSNs: 0964-5691

Abstract

Abstract: Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km 2 ) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82, 000 km 2 ) can provide improved predictions compared to data-rich national scale models (4.2 million km 2 ). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments. Graphical abstract: Image 1 Highlights: Neither regional nor national scale SDMs provided consistently better outputs across all taxa. High performance metric scores did not always correlate to well-differentiated maps. Density outputs consistently produced refined maps across spatial scales. A Multi-scalar density SDM approach provides options for coastal managers. Utilising dual model scales provides information on broad and local environmental drivers to inform coastal management.