Ecosystem services in connected catchment to coast ecosystems: Monitoring to detect emerging trends

There is an increasing need for long-term monitoring of ecosystems and their services to inform on-ground management. The supply of many ecosystem services relies on connections that span multiple ecosystems. Monitoring the underlying condition of interconnected ecosystems is therefore required to track effectiveness of past interventions and identify impending change. Here we test the performance of indicators of ecosystem services with the aim of identifying the time-scales over which indicators of ecosystem services responded to change. We chose a case-study of a catchment in Northern Australia, where water resource development is a threat to the river flows that support vegetation growth and the life-cycle of coastal fishery species. We developed a novel approach to performance testing that drew on state-space modelling to capture ecological dynamics, and structural equation modelling to capture covariation in indicator time series. We first quantified covariation among three ecological indicators that had time-series data: pasture biomass, vegetation greenness and barramundi catch per unit effort. Higher values of all indicators occurred in years with greater river flow. We then predicted the emergence times for each indicator, as the time taken for a trend in an indicator to emerge from the background of natural variation. Emergence times were > 10 years in all cases, quantified at 80 % and higher confidence levels. Past trends and current status of ecosystem service flows are often used by decision makers to directly inform near-term actions, particularly for provisioning services (such as barramundi catch) due to their important contribution to regional economies. We found that ecological indicators could be used to assess historical performance over decadal timespans, but not as short-term indicators of recent change. More generally, we offer an approach to performance testing of indicators. This approach could be useful for quantifying timescales of ecosystem response in systems where cross-ecosystem connections are important.