How does spatial resolution affect model performance? A case for ensemble approaches for marine benthic mesophotic communities

Aim To investigate how changing grid size can alter model predictions of the distribution of mesophotic taxa and how it affects different modelling methods. Location Ningaloo Marine Park, Western Australia. Taxon Benthic mesophotic taxa: corals, macroalgae and sponges. Methods We determined the distributions of the major benthic taxonomic groups: corals, macroalgae and sponges, using a number of modelling techniques and an ensemble using the 'sdm' R package. A range of grid sizes were used (10, 50, 100 and 250 m) to identify how model predictions were altered. Models were evaluated using the area under the curve of a receiver operator characteristic plot (AUC) and the true skill statistic (TSS) using a spatially independent dataset. Results Grid size had a large effect on model performance across the taxonomic groups. Model outputs were compared to null surfaces and 88.8% of models performed significantly better than null. Distribution of corals was best predicted using the finest grid size (10 m) regardless of modelling method, although a model ensemble produced the best results (AUC = 0.80, TSS = 0.52). Macroalgae and sponges were better predicted at coaster grids sizes (250 m). Again, ensembles performed well for both macroalgae (AUC = 0.83, TSS = 0.63) and sponges (AUC = 0.88, TSS = 0.66). Model ensembles maintained high accuracy across grid sizes and were consistently the best, or second-best, performing method. Main conclusions This study has shown how grid size should be considered when producing distribution models. Identifying the most relevant grid size and being aware of the influence it may have will provide more accurate predictions of the distributions of taxa. Ensemble methods maintained good performance across scenarios and thus provide a useful tool for conservation and management especially where single modelling methods showed high levels of variability.