Determining spatial patterns in recreational catch data: a comparison of generalized additive mixed models and boosted regression trees

Abstract

Abstract Marine recreational fisheries (MRFs) are often highly spatially heterogenous, with effort concentrated into small areas, and fisheries spanning large environmental gradients. However, spatially resolved catch data is rarely collected in MRFs, preventing the study of spatial heterogeneity in catch. This study uses recreational catch reported in 10 × 10 nm blocks across eight degrees of latitude in Western Australia to map spatial predictions of the probability of a recreational catch on an average trip for two key species: West Australian dhufish (Glaucosoma hebraicum) and snapper (Chrysophrys auratus). Two spatial modelling techniques are compared for the analysis, generalized additive mixed models (GAMMs) and boosted regression trees (BRTs). We find that BRTs outperform GAMMs, but performance gains are small. We also find marked spatial variations in recreational catch probabilities: high catches of dhufish are found in the north of the study area, and low catches in the Perth Metropolitan area and in the south; snapper catches are highest in the north and low in the south. These patterns are used to identify important spatial processes in the fishery. The analysis also suggests that modelling approach (GAMMs or BRTs) has only a minor effect on outcomes of spatial catch analysis in MRFs.