Error, Power, and Blind Sentinels: The Statistics of Seagrass Monitoring

Abstract

We derive statistical properties of standard methods for monitoring of habitat cover worldwide, and criticize them in the context of mandated seagrass monitoring programs, as exemplified by Posidonia oceanica in the Mediterranean Sea. We report the novel result that cartographic methods with non-trivial classification errors are generally incapable of reliably detecting habitat cover losses less than about 30 to 50%, and the field labor required to increase their precision can be orders of magnitude higher than that required to estimate habitat loss directly in a field campaign. We derive a universal utility threshold of classification error in habitat maps that represents the minimum habitat map accuracy above which direct methods are superior. Widespread government reliance on blind-sentinel methods for monitoring seafloor can obscure the gradual and currently ongoing losses of benthic resources until the time has long passed for meaningful management intervention. We find two classes of methods with very high statistical power for detecting small habitat cover losses: 1) fixed-plot direct methods, which are over 100 times as efficient as direct random-plot methods in a variable habitat mosaic; and 2) remote methods with very low classification error such as geospatial underwater videography, which is an emerging, low-cost, non-destructive method for documenting small changes at millimeter visual resolution. General adoption of these methods and their further development will require a fundamental cultural change in conservation and management bodies towards the recognition and promotion of requirements of minimal statistical power and precision in the development of international goals for monitoring these valuable resources and the ecological services they provide.

Seagrass recovery after fish farm relocation in the eastern Mediterranean

“…Our permanent P. oceanica monitoring systems are the first in Cyprus and the easternmost seagrass PMN systems in the Mediterranean. Setting up monitoring systems using permanent cement markers is a durable and effective method to monitor the edge of seagrass meadows from fixed positions over medium to long timeframes (Pergent et al, 2015) and is substantially more robust than random plots for monitoring seagrasses (Schultz et al, 2015).…”

“…Comparable temporal monitoring along the edge of the meadow is possible through photography and measurements of vitality parameters from fixed positions. Slow growing seagrasses such as P. oceanica are especially suited to fixed-plot monitoring (Schultz et al, 2015) as small-spatial scale progression or regression of the meadow can be monitored effectively.…”

“…They can also be compared with other PMN systems set up in other places across the Mediterranean Sea to assess the P. oceanica population dynamics in different regions. The use of fixed plot methods using cement markers, like the PMN method applied in the Mediterranean or using quadrats placed over transects like the SeagrassNet method applied globally (www.SeagrassNet.org), allow reliable and effective microscale monitoring of seagrass descriptors from the same positions using standardised methodologies, have high statistical power, and their use should be encouraged and widely adopted in generic ecosystem monitoring (Schultz et al, 2015). The disadvantage with the used P. oceanica descriptors is that they respond late to generalised pressures.…”

Toward a Coordinated Global Observing System for Seagrasses and Marine Macroalgae

“…Remote sensing and in situ sampling provide complementary views of seagrass habitat. Remote sensing across heterogeneous reporting areas often cannot detect habitat distribution changes <30% (Lee Long et al, 1996;Unsworth et al, 2009;Hossain et al, 2010;Schultz et al, 2015). Such programs require groundtruthing to increase precision and accuracy.…”

“…There is an urgent need to design more effective monitoring capable of detecting change of 10% or less (Duarte, 2002). Approaches that use fixed plots have higher statistical power for detecting small changes compared with random-plot methods (Schultz et al, 2015). The tiered approach that links remote sensing to in situ sampling systematically offers promising solutions to this challenge (Neckles et al, 2012).…”