DOI: 10.5061/dryad.vdncjsxtc
title: The modeled distribution of corals and sponges surrounding the Salas y Gómez and Nazca ridges with implications for high seas conservation

description: The Salas y Gómez and Nazca ridges are two adjacent seamount chains off
 the west coast of South America that collectively contain more than 110
 seamounts. The ridges support an exceptionally rich diversity of benthic
 and pelagic communities, with the highest level of endemism found in any
 marine environment. Despite some historical fishing in the region, the
 seamounts are relatively pristine and represent an excellent conservation
 opportunity to protect a global biodiversity hotspot before it is
 degraded. One obstacle to effective spatial management of the ridges is
 the scarcity of direct observations in deeper waters throughout the region
 and an accompanying understanding of the distribution of key taxa. Species
 distribution models are increasingly used tools to quantify the
 distributions of species in data-poor environments. Here, we focused on
 modeling the distribution of demosponges, glass sponges, and stony corals,
 three foundation taxa that support large assemblages of associated fauna
 through their creation of complex habitat structures. Models were
 constructed at a 1 km2 resolution using presence and pseudoabsence data,
 dissolved oxygen, nitrate, phosphate, silicate, aragonite saturation
 state, and several measures of seafloor topography. Highly suitable
 habitat for each taxa was predicted to occur throughout the Salas y Gómez
 and Nazca ridges, with the most suitable habitat occurring in small
 patches on large terrain features such as seamounts, guyots, ridges, and
 escarpments. Determining the spatial distribution of these three taxa is a
 critical first step towards supporting the improved spatial management of
 the region. While the total area of highly suitable habitat was small, our
 results showed that nearly all of the seamounts in this region provide
 suitable habitats for deep-water corals and sponges and should therefore
 be protected from exploitation using the best available conservation
 measures.
description: Geo-referenced coral and sponge
 records were obtained from the Ocean Biodiversity Information System (OBIS
 2020) the NOAA Deep-Sea Coral and Sponge Database (NOAA 2020), and
 supplemented with records from recent expeditions to the area (J López and
 E Easton, 2020, unpublished data). All records were obtained as
 presence-only records, with duplicate records removed prior to analysis.
 The bulk of records were focused on the Salas y Gómez and Nazca ridges,
 with another cluster of records in the neighboring Juan Fernández Islands
 region. We chose to focus on three higher taxonomic groupings that are
 often key foundation species on seamounts: stony corals (Order
 Scleractinia, n=233), demosponges (Order Demospongiae, n=275), and glass
 sponges (Order Hexactinellida, n=134). Within the study area, a suite of 44
 environmental variables known to influence the distribution of corals and
 sponges were constructed for use in models (Table 1). Bathymetry for the
 study area were obtained from the SRTM30+ dataset (Becker et al. 2009;
 Sandwell et al. 2014) at a native resolution of 0.0083° (approximately 1
 km) and used in the creation of several additional layers. A number of terrain metrics were derived
 from this bathymetry layer to define the shape of the seafloor. Slope,
 roughness, aspect, general curvature, cross-sectional curvature, and
 longitudinal curvature were calculated using the ArcGIS (v10.8, ESRI)
 toolkit ‘DEM Surface Tools’ (v2; Jenness 2004, Jenness 2013a). Slope was
 measured in degrees and calculated using the 4-cell method (Jones 1998).
 Aspect represents the compass direction of the steepest slope and was
 converted to an index of eastness using a sine transformation and an index
 of northness using a sine transformation. Curvature metrics assess the
 likely flow of water across a feature, with positive values generally
 indicating convex features that cause water to accelerate and diverge, in
 contrast to concave features where water would be expected to decelerate
 and converge. Roughness is a measure of topographical complexity,
 calculated here as the ratio of surface area to planimetric area, with
 more positive values indicating more complex terrain. The Topographic
 Position Index (TPI) was calculated using the toolkit Land Facet Corridor
 Designer (v1.2; Jenness et al. 2013b). TPI assesses the relative height of
 features compared to the surrounding seafloor, with positive areas
 indicating locally elevated features and negative values indicating
 depressions. TPI is scale dependent, and was calculated at scales of 1,000
 m, 5,000 m, 10,000 m, 20,000 m, 30,000 m, 40,000 m, and 50,000 m. Finally,
 the Vector Ruggedness Measure (Hobson 1972; Sappington et al. 2007), which
 calculates terrain heterogeneity, was calculated with a neighborhood size
 of 3, 5, 7, 9, 15, 17, and 21 using the Benthic Terrain Modeler (v3.0;
 Walbridge et al. 2018).   To complement the suite of terrain metrics,
 large-scale geomorphological features expected to provide suitable habitat
 for corals and sponges were obtained from Harris et al. (2014), including
 seamounts, guyots, canyons, ridges, spreading ridges, plateaus and
 escarpments. We applied an algorithmic approach to delineate seafloor
 features based on a modified SRTM+ bathymetry dataset, generating a total
 29 geomorphic categories. See Supplemental Figure 21 for a map of
 geomorphological features in the study area. Data describing benthic conditions at the
 seafloor were obtained from the World Ocean Atlas (v2; 2013), including
 temperature, dissolved oxygen, salinity, nitrates, and phosphates.
 Carbonate data including dissolved inorganic carbon (DIC), total
 alkalinity, and the saturation states of calcite and aragonite, were
 obtained from Steinacher et al. (2009). Current data describing regional
 horizontal and vertical current velocities were obtained from the Simple
 Ocean Data Assimilation model (SODA v3.4.1; Carton et al. 2008).
 Particulate organic carbon (POC) flux to the seafloor was obtained from
 Lutz et al. (2007). Raw benthic data layers were transformed to match the
 extent and resolution of the other environmental variables using an
 upscaling approach that incorporates bathymetry data to approximate
 conditions at the seafloor (<i>sensu </i>Davies and Guinotte
 2011). This upscaling technique has previously been demonstrated to work
 effectively on both global and regional scales for a variety of data
 (Yesson et al. 2012; Georgian et al. 2019). Surface conditions were assessed as
 chlorophyll <i>a</i> and mean sea surface temperature data
 obtained from the Aqua MODIS program (Aqua MODIS 2018). Both layers were
 calculated as the mean value from 2002-2016 at a native resolution of 4
 km, and resampled to match the extent and resolution of the other
 environmental layers with no additional interpolation. 
description: Taxonomic data are presented as XX (longitude), YY (latitude),
 and either 0 (psuedoabsence) or 1 (occurrence) for each taxon (stony
 coral, demosponge, or glass sponge) Environmental data
 are ascii rasters in the geographic coordinate system WGS84 with a grid
 size of 0.008333333333.