Combined species occurrence and density predictions to improve marine spatial management

Abstract

Spatial information on the distribution and densities of key species is an important prerequisite for understanding the functioning and management of ecosystem services. Species distribution models (SDMs) are increasingly used in marine environments to assist with spatial management; however, most SDMs only predict occurrence and not density. Here, we use SDMs to predict probability of occurrence and density of two key estuarine bivalve species (Austrovenus stutchburyi and Paphies australis) that differ in habitat usage and distribution, to gain insight into the utility of these methods for management. Boosted regression trees (BRTs) were used to predict occurrence, density, and uncertainty at a fine spatial scale (100 m resolution). Results showed high probability of occurrence for Paphies near the estuary mouth (up to 0.83), where high densities, exceeding 4000 ind m−2, were also predicted. For Austrovenus, predicted occurrence was high throughout the intertidal area, ranging from 0.5 to 0.87, with no clear spatial patterns. Instead, density models clearly identified spatial patterns for Austrovenus, with high densities exceeding 1000 ind m−2. Spatially explicit uncertainty was low throughout the estuary for both species, providing confidence in model outcomes. Our study demonstrates that a high probability of occurrence does not necessarily equate to high density and illustrates the need for the transition to more informative species density models. Management that simultaneously considers both density and occurrence probabilities will enable targeted protection of areas that are of greatest ecological value to species of interest.