A probabilistic sampling strategy for estimating plant density in Posidonia oceanica meadows.

Marine and coastal ecosystems, such as seagrasses, mangroves, and coral reefs, provide a range of essential provisioning, regulating and cultural ecosystem services. Recent United Nations guidelines on ecosystem accounting (SEEA EA) emphasise the need for biophysical data as the foundation for compiling ecosystem accounts and conducting economic evaluations for developing indicators and informing policies and interventions. However, data availability on marine ecosystems is limited with respect to terrestrial ones. Moreover, the collection of biophysical data on marine ecosystem extent and condition required for ecosystem accounting (EA) is often not aligned with existing habitat monitoring strategies. This study aims to address the scarcity of spatial data on marine ecosystems and facilitate the integration of current monitoring strategies with the scope of EA. We propose the application of design-based inference for the estimation, mapping, and monitoring of key ecological attributes of marine ecosystems. We focus on the habitat of Posidonia oceanica, an endemic seagrass of the Mediterranean Sea, but the proposed strategy is adaptable to other ecosystems. The benefits of appropriate probabilistic sampling schemes for assessing P. oceanica are explored via simulation testing. The performance of different sample schemes in artificial populations reveals that reliable estimates of density (as well as their precision) can be obtained even with low sample sizes. The empirical viability of our methodology is exemplified using data collected on a meadow located in an Italian Marine Protected Area (Puglia region, Southern Italy).