Nissa Kreidler | USGS Science

USGS2025

A comparison of deep-sea coral and sponge species distribution models and the impact of ocean currents in the Southern California Bight

Deep-sea corals and sponges (DSCS) are signature taxa of deep-water habitats, but ecological mechanisms that drive their geographic distributions can be difficult to uncover due to the challenges of surveying deep-water ecosystems and limited oceanographic data. A comparison of species distribution models was made for three DSCS ( Antipathes dendochristos, Plumarella longispina, and an unidentified sponge morphotype known as Porifera #2), two of which are associated with young-of-the-year and adult rockfish taxa ( Sebastes spp.). Models were built using generalized additive models (GAMs) that accounted for spatial autocorrelation. These models were compared to Maxent models, a commonly used method. All models utilized bathymetrically derived variables (including depth, slope, and Bathymetric Position Index) and output from a coupled physical-biogeochemical ocean model (including current direction and magnitude, food abundance, and dissolved oxygen). Direction of benthic ocean currents played an important role in predictions, pointing to large-scale ecological processes that may determine presence of DSCS. GAMs outperformed Maxent models and predicted more ecologically interpretable patterns. Additionally we quantified the predicted amount of suitable habitat that is currently located within some protected areas of the Southern California Bight and show that many hot spots occur outside protected areas. Given the importance of DSCS for multiple demersal fish species of commercial or conservation concerns, this research provides valuable information that natural resource managers can use as a tool in the Southern California Bight.

California↗

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↗