Cooperative Fish and Wildlife Research Units Program: California
Education, Research and Technical Assistance for Managing Our Natural Resources

Winship A.J., Thorson J., Clarke E., Coleman H., Costa B., Georgian S., Gillett D., Grüss A., Henderson M., Hourigan T.F., Huff D., Kreidler N., Pirtle J., Olson J.V., Poti M., Rooper C.N., Sigler M.F., Viehman S., and Whitmire C.E. in review . Good practices for species distribution modeling of deep-sea corals and sponges: data collection, analysis, validation, and communication. Submitted to Frontiers in Marine Science.


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 provide recommendations of 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 recommend integrating available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys). When possible, we recommend models fitted to presence-absence and density data rather than 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. We also recommend that analysts include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and should be 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 recommendations in real-world settings.