Identifying optimal values of coastal habitat condition for management and restoration

Fish assemblages are considered valuable monitoring targets for coastal and marine ecosystems because their populations can reflect ecosystem condition. Human impacts to coastal seascapes are pervasive, necessitating management and restoration actions that are assessed against quantitative targets. Relationships between metrics of habitat condition and animal assemblages can be used to identify where management interventions should aim to maximise outcomes for key functions and services. In this study, we surveyed fish assemblages (using underwater videography) and habitat condition (focusing on the size, composition and complexity of habitats) of five estuarine ecosystems (mangrove forests, seagrass meadows, saltmarsh, log snags and rocky outcrops) across 13 estuaries over two years (for n = 981 sites) in southeast Queensland, Australia. We used generalised additive models to quantify relationships and identify thresholds between metrics indexing habitat condition and key metrics of fish assemblages (species richness and abundance), and used these models to calculate optimal habitat condition values that maximise habitat value for fish and fisheries. We also identified indicator fish species whose abundance correlated with these optimal values. Metrics of habitat condition that index food availability for fish (such as algae and oyster cover) or broader habitat complexity (such as seagrass height or mangrove canopy cover) were consistently important for fish biodiversity and abundance. Relationships were readily translated into optimal values for key metrics of habitat condition for four of the five ecosystems surveyed. We propose that incorporating optimal values of habitat condition into management would maximise the abundance of key indicator fish species, including several which are important fisheries targets. The findings of this study will help managers to identify the optimal condition requirements of coastal ecosystems, thereby maximising the effectiveness and efficiency of often sparse management resources.