MM-23-03: Accounting for Scale Bias in Marine Minerals Studies

Currently, many marine minerals field studies produce results based on data collected in a discrete, ecologically arbitrary footprint (i.e., a dredge or leasing area) over a relatively short period of time (2-4 years). While these offer valuable insight into site-specific conditions and responses, analyzing these data at finer or coarser scales, often integrating external datasets (e.g., species distribution relative to regional stratification) is often limited due to scope, data availability, and budget limitations. With several robust datasets focused on the ecology before and after marine minerals activities, it is an opportune time to re-evaluate past studies and proactively consider future methods. BOEM has identified the need to better understand how the spatial and temporal scales (explicit or implicit) in the study designs, sampling strategies, and analytical or statistical methods underlying BOEM’s biological and ecosystem studies can implicate results, findings, and interpretations. The purpose of this study is to evaluate past MMP studies and provide guidance on how to plan studies at the appropriate scale or nested scales. This will ensure that BOEM is maximizing the design, methods, and application of environmental studies for assessments and decisions.

Objectives include identifying how well the spatial and temporal scales of MMP research and authorized activities match (or mismatch) the scales of habitat and organismal distribution and naturally occurring phenomena that may substantially influence habitat or organismal distribution. With this information, this study will provide recommendations and propose appropraite methods or approaches that consider relevant scales for future MMP research.

The Study Area extends from the offshore extent of state jurisdiction (3 nm from shore off most states, except for the west coast of FL and TX, where it starts at 9 nm) to 50 m deep along the Atlantic and Gulf of Mexico. It may include state water areas depending on data availability and relevance.

Deadline: Aug. 7, 2023