Matthew Poti | USGS Science

USGS2022

Workshops report for mesophotic and deep benthic community fish, mobile invertebrates, sessile invertebrates and infauna

Two workshops with subject matter experts in the appropriate fields, were held in November and December 2021 to elicit guidance and feedback from the broader mesophotic and deep benthic scientific community. These workshops focused on best practices/approaches and identifying data gaps relative to habitat assessment and evaluation goals of the Mesophotic and Deep Benthic Community (MDBC) restoration portfolio. The first workshop was a combined effort of the Habitat Assessment and Evaluation (HAE) Project Team and the Deepwater Horizon (DWH) Program. Industrial Economics, Inc. (IEc) provided extensive workshop planning, organizing, execution, and facilitation support during all stages of the workshop. Based on a questionnaire sent to scientists in August, 2021, the workshop focused on fish and mobile invertebrate habitat associations, abundance trends, community metrics, and food web functionality. Topical presentations and discussions focused not only on demersal fish and mobile invertebrates that are directly associated with mesophotic and deep benthic habitats, but also considered water column species and communities that benefit from these habitats more broadly. The second workshop, intended to complement the first workshop, focused on identifying best practices and critical information gaps for key community metrics, larval dispersal modeling, connectivity, effects and variability of environmental parameters, and recovery trajectories of corals, infauna, and other sessile invertebrates. Through literature review, internal HAE scientists considered these topics to be critical for restoration success. Products from the literature review included topical summaries (see Appendix B) that summarized the current state-of-the-science and provided the framework for the workshop. Information generated from the workshops will assist the MDBC HAE Project, and more broadly the DWH Program, identify data gaps and develop a suite of best practices for restoration activities.

DWH MDBC Summary Report↗

USGS2020

Good practices for species distribution modeling of deep-sea corals and sponges for resource management: Data collection, analysis, validation, and communication

Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, we suggest analysts: 1) integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys); and 2) include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty. When possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.

Frontiers in Marine Science↗