High-Resolution Habitat Suitability Models for the Conservation and Management of Vulnerable Marine Ecosystems on the Louisville Seamount Chain, South Pacific Ocean

Abstract

Vulnerable marine ecosystems (VMEs) are ecosystems at risk from the effects of fishing or other kinds of disturbance, as determined by the vulnerability of their components (e.g., habitats, communities, or species). Habitat suitability modeling is being used increasingly to predict distribution patterns of VME indicator taxa in the deep sea, where data are particularly sparse, and the models are considered useful for marine ecosystem management. The Louisville Seamount Chain is located within the South Pacific Regional Fishery Management Organization (SPRFMO) Convention Area, and some seamounts are the subject of bottom trawling for orange roughy by the New Zealand fishery. The aim of the present study was to produce high-resolution habitat suitability maps for VME indicator taxa and VME habitat on these seamounts, in order to evaluate the feasibility of designing within-seamount spatial closures to protect VMEs. We used a multi-model habitat suitability mapping approach, based on bathymetric and backscatter data collected by multibeam echo sounder survey, and data collected by towed underwater camera for the stony coral and habitatforming VME indicator species Solenosmilia variabilis, as well as two taxa indicative of stony coral habitat (Brisingida, Crinoidea). Model performance varied among the different model types used (Boosted Regression Tree, Random Forest, Generalized Additive Models), but abundance-based models consistently out-performed models based on presence-absence data. Uncertainty for ensemble models (combination of all models) was lower overall compared to the other models. Maps resulting from our models showed that suitable habitat for S. variabilis is distributed around the summitslope break of seamounts, and along ridges that extend down the seamount flanks. Only the flat, soft sediment summits are predicted to be unsuitable habitat for this stony coral species. We translated a definition for stony coral-reef habitat into a S. variabilis abundance-based threshold in order to use our models to map this VME habitat. These maps showed that coral-reef occurred in small and isolated patches, and that most of the seabed on these seamounts is predicted to be unsuitable Rowden et al.High-Resolution VME Habitat Suitability Maps habitat for this VME. We discuss the implications of these results for spatial management closures on the Louisville Seamount Chain seamounts and the wider SPRFMO area, and future modeling improvements that could aid efforts to use habitat suitability maps for managing the impact of fishing on VMEs.

Climate‐induced changes in the suitable habitat of cold‐water corals and commercially important deep‐sea fishes in the North Atlantic

“…Finally, ensemble HSI and uncertainty were computed for all species and for the two study periods by calculating the average of these two indexes by cell after weighting the three families of model outputs with the evaluation metrics AUC and TSS, using the same approach as Rowden et al (). We estimated the importance of each predictor variable to the ensemble habitat suitability model predictions as the average of the variable importance in the individual models weighted by the models' evaluation metrics.…”

“…Repeating this process 100 times for each model yielded 100 estimates of HSI for each cell. Finally, we computed the coefficient of variation (CV) of the bootstrap output for each species, modelling approach, and the present-day predictions and future projections.Finally, ensemble HSI and uncertainty were computed for all species and for the two study periods by calculating the average of these two indexes by cell after weighting the three families of model outputs with the evaluation metrics AUC and TSS, using the same approach asRowden et al (2017). We estimated the importance of each predictor variable to the ensemble habitat suitability model predictions as the average of the variable importance in the individual models weighted by the models' evaluation metrics.…”

Determining Coral Density Thresholds for Identifying Structurally Complex Vulnerable Marine Ecosystems in the Deep Sea

“…Imagery from both cameras was analyzed to obtain data on the abundance and distribution of S. variabilis and all visible epifauna. These data were used to not only test regional habitat suitability models, but to also make abundance-based models of S. variabilis (Rowden et al, 2017).…”

“…Most methods used to date to distinguish VMEs involve some subjective judgment, and therefore the veracity of these methods depends upon a number of untested assumptions. For example, Rowden et al (2017) used abundance-based habitat suitability models of a coral species and a subjective density-based definition of a coral reef to predict VME distribution. However, the definition used was not related directly to any of the criteria that the FAO guidelines specify for what constitutes a VME (FAO, 2009).…”

“…Thresholds for identifying cold-water coral reefs are beyond the remit of the FAO guidelines, and of the few thresholds that have been published independently none are based on a quantitative analysis of the direct relationship between coral density and associated biodiversity. These threshold estimates for identifying distinct coral reef habitat range widely between 15 and >60% coral cover (Vertino et al, 2010;Rowden et al, 2017), presumably at least in part because of taxa and site/regional differences in the relationship between coral cover and the formation of a recognizable reef structure, but also potentially because of the subjective nature of the threshold estimate. Ideally, if habitat suitability models or even direct observation of VMEs are going to be used to inform spatial management of bottom trawling, then studies are required that objectively derive VME thresholds where VME indicator taxa densities are sufficient to provide specific ecological functions.…”