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

Area of Science:

• Marine Ecology
• Ecosystem Accounting
• Conservation Science

Background:

• Marine and coastal ecosystems provide vital services, but biophysical data for ecosystem accounting (EA) is scarce compared to terrestrial systems.
• Current habitat monitoring strategies often don't align with EA requirements for spatial data on marine ecosystem extent and condition.
• The United Nations' System of Environmental-Economic Accounting (SEEA) highlights the need for robust biophysical data for policy and intervention.

Purpose of the Study:

• To address the scarcity of spatial data for marine ecosystem accounting.
• To facilitate the integration of current monitoring strategies with EA scope.
• To propose and validate a methodology for estimating and mapping marine ecosystem attributes.

Main Methods:

• Application of design-based inference for estimation, mapping, and monitoring of marine ecosystem attributes.
• Focus on Posidonia oceanica seagrass meadows, with adaptability to other ecosystems.
• Simulation testing to explore benefits of probabilistic sampling schemes and assess performance with varying sample sizes.

Main Results:

• Design-based inference can yield reliable estimates of ecological attributes, such as density, with quantifiable precision.
• Simulation testing demonstrated that accurate estimates are achievable even with low sample sizes.
• Empirical validation using data from a Posidonia oceanica meadow in an Italian Marine Protected Area confirmed the methodology's viability.

Conclusions:

• Design-based inference offers a robust framework for the spatial assessment and monitoring of marine ecosystems.
• The proposed strategy enhances the integration of ecological monitoring with ecosystem accounting frameworks.
• This approach supports informed policy-making and conservation interventions for marine environments.