Although the public desire for healthy environments is clear-cut, the science and management of ecosystem health has not been as simple. Ecological systems can be dynamic and can shift abruptly from one ecosystem state to another. Such unpredictable shifts result when ecological thresholds are crossed; that is, small cumulative increases in an environmental stressor drive a much greater change than could be predicted from linear effects, suggesting an unforeseen tipping point is crossed. In coastal waters, broad-scale seagrass loss often occurs as a sudden event associated with human-driven nutrient enrichment (eutrophication). We tested whether the response of seagrass ecosystems to coastal nutrient enrichment is subject to a threshold effect. We exposed seagrass plots to different levels of nutrient enrichment (dissolved inorganic nitrogen) for 10 months and measured net production. Seagrass response exhibited a threshold pattern when nutrient enrichment exceeded moderate levels: there was an abrupt and large shift from positive to negative net leaf production (from approximately 0.04 leaf production to 0.02 leaf loss per day). Epiphyte load also increased as nutrient enrichment increased, which may have driven the shift in leaf production. Inadvertently crossing such thresholds, as can occur through ineffective management of land-derived inputs such as wastewater and stormwater runoff along urbanized coasts, may account for the widely observed sudden loss of seagrass meadows. Identification of tipping points may improve not only adaptive-management monitoring that seeks to avoid threshold effects, but also restoration approaches in systems that have crossed them.

Seagrasses are in decline globally due to sustained pressure from coastal development, water quality declines and the ongoing threat from climate change. The result of this decline has been a change in coastal productivity, a reduction in critical fisheries habitat and increased erosion. Attempts to slow this decline have included legislative protection of habitat and direct restoration efforts. Monitoring the success of these approaches requires tracking changes in the abundance of seagrasses, but such monitoring is frequently conducted at either too coarse a spatial scale, or too infrequently to adequately detect changes within individual meadows. Here, we used high resolution aerial imagery to quantify the change in meadows dominated by Posidonia australis over five years at 14 sites in five estuaries in south-eastern Australia. Australia has some of the world's most diverse and extensive seagrass meadows, but the widely distributed P. australis has a slow growth rate, recovers poorly after disturbance, and suffers runaway attrition if the conditions for recovery are not met. In 2010, after declines of 12–57% between the 1940s and 1980s, P. australis was listed as a threatened ecological community in New South Wales. We quantified changes in area at fine spatial scales and, where loss was observed, describe the general patterns of temporal decline within each meadow. Our results demonstrate that seagrass meadows dominated by P. australis underwent declines of ~ 2–40% total area at 11 out of 14 study sites between 2009 and 2014. In the iconic Sydney Harbour, our analyses suggest that P. australis meadows are declining at an average rate greater than 10% yr-1, exceeding the global rate of seagrass decline. Highlighting these alarming declines across the study region should serve as means to prioritise management action and review the effectiveness of legislative listing as a method to limit impacts at an ecosystem level.

Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high‐resolution imaging and associated machine‐learning image‐scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free‐to‐use machine‐learning software to semi‐automatically generate dense and widespread abundance records of a habitat‐forming algae over ~5,000 m2 of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver‐based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10–20 transects (50 × 1 m) were required to obtain reliable results. This represents 2–20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine‐resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi‐automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.

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