Substrate mapping to inform ecosystem science and marine spatial planning around the main Hawaiian Islands

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Substrate mapping to inform ecosystem science and marine spatial planning around the main Hawaiian Islands ¨ss3, T. Acoba2,4 and J.R. Smith5 D. Dove1,2, M. Weijerman2, A. Gru 1

British Geological Survey, Edinburgh, United Kingdom 2Pacific Island Fisheries Science Center, NOAA, Honolulu, HI, United States 3School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, United States 4Joint Institute for Marine and Atmospheric Research, University of Hawai‘i, Honolulu, HI, United States 5Department of Oceanography, SOEST/University of Hawai‘i at M¯anoa, Honolulu, HI, United States

Abstract Shallow coral reef and seamount-based ecosystems associated with Pacific islands and atolls host a high and abundant biodiversity, yet many of the ecosystems are threatened by a range of climatic, oceanographic, and anthropogenic stresses. In these types of environments, the morphology and composition of the seabed have been shown to be useful proxies/surrogates for the distribution and abundance of benthic organisms, as well as the other organisms and communities that depend on them. However, currently-available maps of substrate character across these ecosystems are either absent or insufficiently accurate to adequately support ecosystem science and marine spatial planning. We developed classification approaches for substrate mapping in coral reef and deeper-water environments (0 500 m) around the main Hawaiian Islands (MHI) based on multibeam echosounder bathymetry and backscatter, utilizing several key morphological variables together with the backscatter data in an unsupervised classification. Predicted substrate composition (i.e., hard vs soft) reflects the geomorphological character of the seafloor, as the morphology and backscatter-response of the seafloor showed clear associations with diverse features that have been formed over a range of temporal and spatial scales. The resulting substrate maps, together with further seafloor metrics, were then used within species distribution models to predict the biomass distribution of several functional groups (benthic associated fish species) within the MHI. While depth was estimated to be the most important predictor of biomass distribution, substrate composition and several morphological metrics (at different spatial scales) were also found to be significant predictors. Independent of this, the biomass distribution maps (as well as the seafloor substrate maps) will provide needed spatial information to support marine spatial planning, as well as further ecosystem science (e.g., commercially-important bottomfish communities).

Seafloor Geomorphology as Benthic Habitat. DOI: https://doi.org/10.1016/B978-0-12-814960-7.00037-3 © 2020 Elsevier Inc. All rights reserved.

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620 Chapter 37 Keywords: Pacific islands and atolls; Hawai‘i; mesophotic coral ecosystems; hard substrates; benthic habitat; ecosystem modeling

Introduction Marine environments surrounding many Pacific islands and atolls host extraordinary biodiversity and abundance (Roberts et al., 2002), with coral reefs playing a fundamental role, exerting a disproportionately large influence on Earth’s natural systems and the biogeographic distribution of life (e.g., Birkeland, 1997). The biodiversity around the Hawaiian Archipelago is notably less than that observed at many locales closer to the Coral Triangle in the Western Pacific; however, because of its remoteness, Hawaii supports very high levels of marine endemism (e.g., Grigg et al., 2008). Within the main Hawaiian Islands (MHI) (Fig. 37.1), coral reef ecosystems support significant economic activity (e.g., Cesar and van Beukering, 2004), and are also fundamentally important to many island communities and indigenous culture (e.g., Kittinger et al., 2016). Pacific Island marine ecosystems (including MHI ecosystems) are, however, facing a variety of climate, oceanographic and anthropogenic stressors, including: coral bleaching, overfishing, coastal development, and nutrient pollution (Hoegh-Guldberg et al., 2007; Williams et al., 2015; Hughes et al., 2017). These stressors are causing diverse changes in reef ecosystems, broadly resulting in net reef degradation (e.g., Gove et al., 2016; Brainard et al., 2018). For example, the biomass of piscivorous fish (e.g., tunas, sharks) within the densely populated MHI region was found to be only approximately 5% of that observed in the Northwest Hawaiian Islands, where there is relatively little human influence (Williams et al., 2011). Below the shallow reefs (0 2 30 m depth), seamounts support further important ecosystems within intermediate depths (e.g., de Forges et al., 2000; Clark et al., 2010). Mesophotic coral reef ecosystems (approximately 30 150 m depth) support less diversity and abundance yet play significant roles in fish migration and coral recruitment. They may also provide a refuge for coral colonies as shallow reefs come under increasing stress (e.g., Rooney et al., 2010; Pyle et al., 2016). Looking deeper still, commercially-important bottomfish habitats extend down to around 400 m depth (Richards et al., 2016), and Hawaiian black coral species have been found down to around 3000 m depth, with specimens living up to B4000 years (Roark et al., 2009).

Naturalness, condition, and trend The condition of coral reef ecosystems is influenced by many natural factors (e.g., sea surface temperature, wave exposure, productivity), yet, in the last 100 years, anthropogenic

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Figure 37.1 Bathymetry of the main Hawaiian Islands (MHI) within the Pacific Ocean. Study area incorporates all seafloor areas between 0 and 500 m depth (rainbow color-scale; 5 m spatial resolution). Background bathymetry covers full ocean depths (blue color-scale; 50 m spatial resolution). Large geomorphic features are indicated. Substrate mapping and species distribution modeling were based on multibeam echosounder (MBES) bathymetry and backscatter data down to 500 m depth. The blue box indicates the inset area for Fig. 37.2, while the red box indicates the inset area for Fig. 37.3.

activities have also greatly diminished the condition of shallow water coral reef ecosystems (Rodgers et al., 2010; Friedlander et al., 2008; Williams et al., 2015), which further deteriorated during the 2014 16 global “bleaching” event due to ocean warming when corals lost their symbiotic algae that provided them with their energetic needs. This resulted in up to 70% coral mortality along some of Hawai‘i’s coastlines (Kramer et al., 2016). With the continuing warming of the ocean, bleaching events are predicted to become more frequent, possibly occurring annually by the mid-21st century, in which case it would be extremely unlikely corals would have time to recover (van Hooidonk et al., 2016). Based on these selected observations, shallow coral reef habitats in the MHI region are graded as “Poor (3 4).” Research on deeper reefs and seamounts has only just begun so there are no

622 Chapter 37 appropriate baselines for comparison. However, because the ocean is acidifying and warming, marine debris (mostly fishing gear) has been observed at greater depths (especially in the mesophotic environment), and there is a fishing industry for deep-sea corals, lobster and bottomfish species; we tentatively suggest that the deeper habitats are presently in “Good (5 6)” condition compared to the reference year of 1900. The MHI are situated in the oligotrophic (nutrient poor) subtropical gyre of the North Pacific. Decadal oceanographic changes are driven by the North Pacific Gyre Oscillation that respond to regional and basin-scale variations in wind-driven upwelling and horizontal advection (Di Lorenzo et al., 2008). Northeast trade winds occur throughout the year and are strongest from April to November (Ogston et al., 2004). Phytoplankton biomass near islands or seamounts are influenced by the interaction of biological productivity and ocean current 2 bathymetric interactions (Gove et al., 2016). The delivery of inorganic and organic nutrients that serve as critical energy sources for biological processes within coral reef ecosystems, are influenced by upwelling of deep, cool waters via internal waves and related to bathymetry (Gove et al., 2016). One aspect that most, if not all coral reef and seamount-based ecosystems have in common, across all depths, is a level of dependence on substrate morphology and composition (Knowlton, 2001; Clark et al., 2010). Hard substrates appear to be particularly important as they are associated with higher diversity and abundance of benthic organisms and the other organisms that directly and indirectly depend on these habitats. This association with hard substrates is similar in many other marine biomes, occurring within diverse biogeographic systems (e.g., Taylor and Wilson, 2003; McArthur et al., 2010; Davies and Guinotte, 2011; Buhl-Mortensen et al., 2012). The presence of hard substrates can largely be predicted by seafloor geomorphology, which reflects the interaction between the near-surface geology and a range of oceanographic (e.g., bottom currents), sedimentary, and biological processes (e.g., reef building organisms) occurring over a range of temporal and spatial scales (e.g., from tidal, to millennial orbitally-paced sea level cycles; Dove et al., 2016). However, in most of the coral reef and seamount-based ecosystems, there is typically very little information on the spatial distribution of hard substrates, particularly in waters deeper than 30 m. The objective of this study was to develop substrate mapping approaches, dependent on geomorphology information, that can be consistently applied across coral reef (shallow and mesophotic) and deeper seamount ecosystems in both the MHI and the Pacific Remote Islands National Marine Monument (PRIMNM) (Dove et al., 2018). Here, we present results for the MHI only (Fig. 37.1). We also generate further seafloor metrics (at multiple spatial scales) to fit species distribution models in order to characterize the biomass distribution of several functional groups (species aggregated based on similar life history characteristics and habitat requirements) within the MHI, described in more detail below.

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Management agencies have shifted from a single species management approach to an ecosystem-based management (EBM) approach, both within the United States (e.g., Pew Oceans Commission, 2003; US Commission on Ocean Policy, 2004; US Executive Order 13547) and internationally (Canada’s Ocean Act, 1996, Voss et al., 2014; Smith et al., 2007). For example, to assess threats to corals listed under the US Endangered Species Act (1973), managers must consider changes to the coral habitat (e.g., hard substrate), oceanographic changes due to climate change, and changes to the herbivore community that facilitate coral recruitment and growth. Another example is bottomfish that rely on hard substrate and high slope areas (Richards et al., 2016). Local changes in habitat or fishing pressure could greatly influence bottomfish stock size; hence accurate habitat information could improve bottomfish stock assessments. Ecosystem simulation models are tools that can help evaluate the cumulative impacts of various stressors on ecosystem services and the structural and functional integrity of ecosystems (e.g., Weijerman et al., 2015). Spatially explicit ecosystem models that support EBM, such as applications of the Atlantis modeling platform (https://research.csiro.au/atlantis/), rely on spatial habitat data and information on functional groups, including their spatial distribution patterns and habitat requirements (Gru¨ss et al., 2016). However, monitoring data of fisheries or from underwater surveys are spatially patchy at best. To produce biomass distribution maps for functional groups, we need to determine relationships between seafloor geomorphology (as characterized by spatially-continuous maps of seafloor morphology and composition) and fish probability-of-encounter and biomass. To estimate these relationships, we fitted generalized additive models (GAMs) to monitoring data for the MHI region. We then generated biomass distribution maps using fitted GAMs, which will be used to parameterize the Atlantis ecosystem model in future analysis.

Geomorphic features The MHI are part of the Hawai‘i-Emperor seamount chain and incorporate eight major islands (from east to west): Hawai‘i, Maui, Kaho‘olawe, L¯ana‘i, Moloka‘i, O‘ahu, Kaua‘i, and Ni‘ihau (Fig. 37.1). The MHI are all products of seamount formation over a fixed Hawaiian hot spot, currently underlying Hawai‘i, aka the “Big Island” (e.g., Clague and Dalrymple, 1987; Wessel and Kroenke, 2008). While fossil reef-terraces are apparent in bathymetry data in deeper water to greater than 1000 m, modern shallow coral reefs around the MHI were formed after the last glaciation (B18 kya), primarily during marine transgression in the mid-Holocene (B8 2 5 kya) (e.g., Grigg, 1998; Grossman and Fletcher, 2004; Rooney et al., 2008). Geomorphic features around the MHI occur over a range of spatial scales (meters 2 100s of km) and were formed by a range of physical processes (e.g., tectonic, volcanic, depositional/erosive, karst, and biogenic). Examples include the

624 Chapter 37 seamounts themselves (and the abyssal plain at their base), to slopes, slide scars and massmovement deposits, submarine canyons, fossil reef-terraces, karst ridges and basins, lava flows, and, of course, the biogenic reefs themselves, with their attendant structures (e.g., fringing reef, spur and groove, escarpments; Grigg et al., 2002; Rooney et al., 2008; Kench, 2014). These features have been further affected (or were directly formed) by climatic and oceanographic processes that also operate over a range of timescales [e.g., from orbitallypaced sea level change (tens of thousands of years), to daily tidal cycles—sediment resuspension]. Within this case study, however, we do not describe the origin or evolution of these geomorphic features, but rather focus on and base our analysis on how geomorphology influences the presence and distribution of hard-substrate (i.e., potential habitat). In waters beyond the penetration depth of satellite imagery (B30 m), we use multibeam echosounder (MBES) bathymetry and backscatter to inform our substrate predictions. Within the past B10 years, there has been an accelerated use of MBES data [particularly bathymetric derivatives (morphological metrics)] within habitat models to improve the predictive accuracy of benthic biodiversity (e.g., Wilson et al., 2007; Dunn and Halpin, 2009; Lecours et al., 2016a,b). Here we adapt these approaches, using several key morphological metrics together with the backscatter data to predict the presence of hard substrates (Fig. 37.2). We then use the resulting hard substrate maps (together with further seafloor metrics) as covariates within GAMs to investigate which seafloor variables (at multiple spatial scales) best predict the spatial patterns in the biomass of some functional groups. The MBES bathymetry and backscatter data used for this study came from multiple vessel surveys and were compiled, synthesized, and gridded to a spatial resolution of 5 m by researchers from the University of Hawai‘i (Smith, 2016). There are several data artefacts that can be expected from compiling datasets from multiple surveys, which were acquired from different systems over multiple years (e.g., occasional bathymetric steps), yet, in general, the quality of the data used in this study is very good. For backscatter data to be employed in MHI-wide assessments, backscatter intensities from all survey datasets were normalized by Smith (2016) to a common scale across the MHI. Despite this protocol, which is quite effective, backscatter intensity boundaries are still observed between survey areas, emphasizing the importance of not basing substrate classification on the backscatter data alone. The study depth has been limited to 500 m within the MHI, which is partly due to the depth ranges of current interest to the fisheries and ecosystem scientists, but also near the depth threshold where the MBES data quality (density of soundings) is insufficient to support gridding at a 5 m spatial resolution. Because the insular shelf break is found between approximately 600 and 1000 m depth, our map area is primarily atop the seamount platform.

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Figure 37.2 Simplified substrate classification approach within the main Hawaiian Islands (MHI; 0 500 m depth). Bathymetry derivatives and backscatter data are preprocessed and run through cluster analysis to identify groups (classes) of shared properties. To produce the final substrate map (‘Hard’ vs ‘Soft’), the mapping practitioner attributes the predicted classes according to the underlying data, and their knowledge of the local seafloor environment.

Hard substrate mapping and seafloor morphology metrics Unfortunately, accurate substrate validation data are sparse to absent within the MHI region. While there are relevant datasets in spatially disparate locations (typically diverbased monitoring programs), the spatial accuracy (e.g., 6 50 m) and limited geographic extent of these data render them unsuitable for classifying high-resolution remote sensing data (i.e., ship-based MBES, 5 m resolution). Therefore, we have utilized an unsupervised machine-learning classification approach (“clustering”) to segregate groups of shared attributes spatially (Fig. 37.2; Jain, 2010; Calvert et al., 2014). After experimenting with a range of morphological attributes (e.g., curvature, slope of slope), our final protocol (implemented within ESRI ArcGIS) employs bathymetric slope,

626 Chapter 37 standard deviation (SD—a measure of rugosity), and relative distance to the mean (RDMV—indicating relative bathymetric highs and lows). Our approach therefore depends on the hypothesis that hard substrates tend to be associated with features that exhibit relatively high slopes and rugosity, and protrude from surrounding seafloor. This concept turns out to be largely reliable within the geomorphic setting of the MHI (seamounts, coral reef dominated, carbonate sand), but may not necessarily apply in other seafloor environments (e.g., sediment-dominated shallow continental shelf). Beyond the morphological attributes, the MBES backscatter is used as the fourth model covariate. Backscatter is the intensity of the acoustic return, providing a proxy for the texture and hardness of the seafloor and, therefore, independently informing on the distribution of sediment and presence of hard substrate (Brown et al., 2011). Prior to the cluster analysis, we applied minor preprocessing to the predictive covariates (MBES backscatter and bathymetric slope, SD, RDMV), thus smoothing the rasters to remove spurious data/noise (focal statistics) and normalizing the covariates to a common scale so covariates are equally weighted within the classification. Clustering is then implemented through the ESRI ArcGIS “ISO Cluster Unsupervised Classification” tool, which combines ISODATA clustering and maximum likelihood classification. The interpreter then chooses the hard/soft threshold between the predicted classes. This is the first subjective step in the mapping process, where the interpreter visually assesses the cluster results to determine whether the predicted boundaries match their observations in the source data and their knowledge of the environment (e.g., hard/soft boundaries accurately predicted between protruding reef and sediment-filled furrows). Ultimately, we produced three alternative hard/soft maps for the MHI (based on clustering to two, four, and seven predicted classes) at 25 m resolution. Each version variably predicts the extent of “hard” according to the different morphological and backscatter characteristics and is judged as valid according to the acoustic data and patchy ground-truth data (i.e., “expert” interpretation). In other words, because we have no way to determine whether one of these maps is more accurate than the others, the different versions are input into the GAMs to determine which version is a better predictor, or alternatively, if different communities respond to different definitions/thresholds of “hard.” The mapping results indicate that hard substrates are commonly linked to high slope/ rugosity areas associated with shallow reefs, karst ridges, fossil reef ridges and escarpments, and canyon walls (Fig. 37.3). Many of these features exhibit high morphological and backscatter values, giving a clear prediction of “hard.” Specific to slope, hard substrates become dominant above approximately 15 20 degrees, and almost everywhere at slopes greater than 30 degrees. This is consistent with the angle of repose for saturated sands observed in empirical and laboratory experiments (e.g., Miller and Byrne, 1966). There are also, however, areas of relatively minor morphological expression (e.g., shallow currentswept platforms), or bathymetric lows, that are predicted as hard on the basis of high

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Figure 37.3 Hard-soft substrate map of subset “Maui Nui” area of the main Hawaiian Islands (MHI). Substrate classes predicted using unsupervised clustering of covariates derived from the multibeam echosounder (MBES) data (bathymetry derivatives and backscatter), and used in the present study to predict the spatial distribution of fish functional-group biomass. Example geomorphic features, and hard/soft associations, are indicated in the figure.

backscatter intensities. For example between Maui and Moloka‘i, hard substrate is associated with outcropping limestone pavement and coralline algae (Fig. 37.3) (Webster et al., 2006). Another example is where high backscatter values predict “hard” within gullies and canyons, likely resulting from the gullies hosting relatively strong density/ gravity currents that either expose underlying bedrock through erosion (or preventing deposition), and/or cause deposition of coarse sediments compared to areas outside of the gullies. The final general scenario that predicts “hard” is low backscatter, and high morphological values. Backscatter data are at times artificially low along very steep slopes and highly-rugose environments (where acoustic energy is reflected and scattered away from the receiver), so the prediction of “hard” in these geomorphic settings is based on sufficiently high morphological metrics. Using backscatter data alone would, therefore, lead

628 Chapter 37 to underprediction of hard in such settings, emphasizing the importance of including the morphological metrics.

Multi scale seafloor metrics As well as providing predicted hard-soft maps to be used as covariates within GAMs, we have also provided the bathymetry (depth), bathymetric derivatives, and backscatter data at several spatial resolutions (5, 25, 125, and 625 m), as it is increasingly recognized that marine organisms respond to these seafloor characteristics at varying spatial scales (e.g., Wilson et al., 2007; Costa et al., 2015; Lecours et al., 2016b). Due to time and computational limitations at the time of running the GAMS, the 5 m seafloor metrics were ultimately not incorporated in the modeling for this study. The bathymetric derivatives provided are: slope, SD, RDMV, northness, and eastness. While the northness and eastness metrics were not used to predict hard versus soft substrates, these aspect variables may serve as potential proxies for local hydrodynamic conditions and related biogeochemical cycles, which cannot be adequately measured at such fine spatial scales.

Biological communities The MHI have coral reef ecosystems fringing all islands, extending to approximately 150 m depth, where light is barely discernible. Historically most coral reef studies have been focused on the shallower (0 30 m) environments, which are the most accessible part of the reef system and have led to considerable information on community assemblages and their spatial distributions (e.g., Costa and Kendall, 2016). The 30 150 m portion of coral reef ecosystems, that is, the mesophotic coral reef ecosystem (MCE), has the potential to function as refugia for shallow species subject to disturbances in the shallow reef zone and has received increased attention in recent years (Pyle et al., 2016; Semmler et al., 2017). Moving deeper still, studies that focus on the subphotic (150 400 m) zone are rare, with the exception of studies on commercially important species, such as bottomfish species (Sackett et al., 2017) and precious coral (Parrish, 2006). Combining survey data from Baited Remote Underwater Cameras, Bottom Camera Bait Stations, manned submersibles, and trawls, we assessed the biological communities at meso- and subphotic depths. The vast majority of species were observed by the submarine surveys (also most numerous), especially in the 300 400 m depth range where the detection limit for cameras is very low due to low light levels. Structural species (i.e., invertebrate species that provide habitat for other species) changed from hard corals (scleractinia; e.g., Leptoseris spp.) that mainly use available light for photosynthesis by the algae incorporated in their tissue to filter-feeding soft corals (e.g., Pleurocorallium niveum) at greater depths. Fish assemblages changed as well along the depth gradient, with many

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shallow coral reef species in the upper mesophotic zone (e.g., parrotfishes, most butterflyfishes and sergeant majors) to slope fishes (Symphysanodon spp.), cusk eels (Pycnocraspedum spp.), and pearlfishes (Carapidae spp.) in the deep subphotic zone. To represent all these species in the Atlantis ecosystem model of the MHI, we aggregated the species according to similar life history characteristics, diet, and habitat requirements in functional groups. Spatially, Atlantis is based on homogeneous areas in terms of oceanographic, habitat and species characteristics. Defining these areas can lead to very large “polygons” or spatial areas on some sites of the islands, whereas other sites have more variability and consist of smaller spatial areas. Atlantis integrates oceanographic, ecological, and socio-economic dynamics with habitat data and simulates key impacts to the ecosystem under study (Fulton et al., 2007); in the present case the insular ecosystem surrounding the MHI. Spatial overlap between human activities (including fishing) and the presence of exploited species and species of concern (e.g., protected species) should be accurately represented in ecosystem models to make the best available predictions of changes in ecosystem state. Due to the sporadic sampling data in the 30 400 m depth range, the spatial distribution patterns of the fish functional groups represented in the Atlantis model for the MHI need to be estimated by fitting GAMs to monitoring, seafloor metrics and other environmental data and then use GAM predictions to produce biomass distribution maps to represent current conditions for Atlantis (Drexler and Ainsworth, 2013; Gru¨ss et al., 2016).

Surrogacy The Atlantis ecosystem model for the MHI represents 58 functional groups, including 21 fish groups. In this study, we focus on two of the fish groups represented in the Atlantis model: the mesophotic benthic carnivore (MBC) group, which includes species such as goatfishes, Hawaiian hogfish (Bodianus albotaeniatus), and snappers; and the mesophotic planktivore (MPL) group, which includes species such as Anthias spp., false Moorish Idol (Heniochus diphreutes) and Chromis spp.

Modeling approach We proceeded in seven steps. First, we compiled monitoring and environmental datasets for statistical modeling, employing data collected by two monitoring programs over the period 2002 09: The University of Hawai‘i Drazen Lab (Moore et al., 2016) and the Pacific Island Fishery Science Center (PIFSC) camera surveys. Both monitoring programs used Bottom Camera Bait Station survey (Merritt et al., 2011) with a spatial accuracy of B35 m. Second, we evaluated the degree of collinearity between continuous environmental covariates using Pearson’s correlation coefficients, leading to the selection of some of these covariates for statistical modeling (Dormann et al., 2013). Third, for each of the two study groups

630 Chapter 37 (i.e., the MBC and MPL groups), we fitted alternative binomial GAMs to encounter/ nonencounter estimates and selected environmental parameters, and then alternative gamma GAMs to nonzero biomass estimates and selected environmental parameters. The “confounding” effects of monitoring program and year were integrated in all GAMs and treated as fixed effect factors (Gru¨ss et al., 2018). After model fitting, if an environmental covariate P-value was greater than .05, we removed the covariate from the GAM and refitted it (e.g., Gru¨ss et al., 2014). For each of the two study groups, nine alternative binomial and gamma GAMs (three groups of three) were fitted: (1) three binomial and gamma GAMs integrating bathymetric and backscatter data at 25, 125, or 625 m resolution and the 2-level hard-soft factor; (2) three binomial and gamma GAMs integrating bathymetric and backscatter data at 25, 125, or 625 m resolution and the 4-level hard-soft factor; and (3) three binomial and gamma GAMs integrating bathymetric and backscatter data at 25, 125, or 625 m resolution and the 7-level hard-soft factor. Due to the extremely large files of the 5 m spatial resolution of the bathymetric and backscatter data, we omitted these in the first round and only include them if the 25 m resolution turned out to be of significance. Fourth, for each of the two study groups, we kept the binomial GAM and the gamma GAM that explained the largest percentage of deviance in the encounter/nonencounter and biomass data, respectively, for further analyses. Fifth, for each of the two study groups, we combined the predictions made by the selected binomial GAM and those made by the selected gamma GAM using the delta method (Lo et al., 1992) and then evaluated the resulting delta GAM using bootstraps and Spearman’s correlation coefficients (Spearman’s ρ’s) between predicted and observed biomass values, as was done in Gru¨ss et al. (2014). Sixth, after the delta GAMs were validated, we assessed the relative importance of covariates in the spatial patterns of probability of encounter and biomass of the MBC and MPL groups, using Thuiller et al.’s (2012) method. Seventh and lastly, we made predictions with the delta GAMs to then produce biomass maps for the MBC and MPL groups usable in the Atlantis ecosystem model.

Results The binomial GAM that was ultimately selected for the MBC group included the effects of depth, slope at 125 m resolution and 4-level hard-soft factor. The binomial GAM that was ultimately selected for the MPL group included the effects of depth and slope at 125 m resolution. Both binomial GAMs explained a moderate percentage of the deviance in the encounter/nonencounter data (39.5% and 40.8%, respectively). The gamma GAM that was ultimately selected for the MBC group included the effects of depth, eastness at 625 m resolution and 2-level hard-soft factor. The gamma GAM that was ultimately selected for the MPL group included the effects of RDMV at 125 m resolution and 2-level hard-soft factor. Both gamma GAMs explained a fair percentage of the deviance in the biomass data

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(57.5% and 45.2%, respectively). The delta GAMs of the MBC and MPL groups both passed the validation test. Depth explained a much larger percentage of the deviance in the encounter/nonencounter and biomass data for the MBC group than all other covariates (Fig. 37.4A and B). Depths ranging from 0 to B175 m have a positive effect on the probability of encounter of the MBC group (Fig. 37.5A). The biomass of the MBC group decreases with depth, and depths greater than B125 m have a negative effect on the biomass of the group (Fig. 37.5B). The relative importance of bottom type (hard-soft factor) in the spatial patterns of probability of encounter of the MBC group is equivalent to that of the monitoring program and year factors (Fig. 37.4A), and the MBC group is more often encountered on hard than on soft

Figure 37.4 Relative importance of predictors in the spatial patterns of (A and C) probability of encounter and (B and D) biomass of the (A and B) mesophotic benthic carnivore (MBC) and (C and D) mesophotic planktivore (MPL) groups predicted by generalized additive models (GAMs). Vertical black lines indicate confidence intervals. RDMV, Relative distance to the mean (see the main text for a definition of this variable).

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Figure 37.5 Smoothed curve of the additive effect to the estimated (A and C) probability of encounter and (B and D) biomass of the mesophotic benthic carnivore (MBC) group for the individual environmental parameters considered in the (A and C) binomial and (B and D) gamma generalized additive models (GAMs) of the functional group. Dotted lines represent 95% confidence intervals, and each mark along the x-axis a single observation. Note that the scale of the y-axis differs from one panel to the next for display purposes.

bottoms (result not shown here). The relative importance of bottom type in the spatial patterns of biomass of the MBC group is greater than that of the monitoring program and year factors (Fig. 37.4B), and the biomass of the MBC group is larger on hard than on soft bottoms (result not shown here). The relative importance of eastness in the spatial patterns

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of biomass of the MBC group is greater than that of the year factor but smaller than that of the monitoring program factor (Fig. 37.4B), and the biomass of the MBC group increases with increasing eastness (Fig. 37.5D). Depth and slope, in that order, explain a larger percentage of the deviance in the encounter/ nonencounter data for the MPL group than the monitoring program and year factors (Fig. 37.4C). Depths ranging from 0 to B200 m have a positive effect on the probability of encounter of the MPL group (Fig. 37.6A), while the probability of encounter of the MPL group tends to increase with increasing slope (Fig. 37.6B). The relative importance of RDMV in the spatial patterns of biomass of the MPL group is smaller than that of the year factor but greater than that of the monitoring program factor (Fig. 37.4D), and the biomass of the MPL group increases with increasing RDMV (Fig. 37.6C). The relative importance of bottom type in the spatial patterns of biomass of the MPL group is smaller than that of both the year factor and monitoring program factors (Fig. 37.4D), and the biomass of the MPL group tends to be slightly larger on hard than on soft bottoms (result not shown here). Using the fitted GAMs and the seafloor metrics for the entire MHI, we generated biomass distribution maps that show the spatial allocation of the biomass of the MBC and MPL groups over the Atlantis spatial domain (Fig. 37.7). These maps and the biomass distribution maps to be generated for the other functional groups represented in the MHI Atlantis ecosystem model will be used in follow-on study to characterize the current status of functional groups spatially.

Discussion and concluding remarks Substrate maps and further derived seafloor morphology metrics were used in GAMs to predict the spatial patterns of encounter/nonencounter and biomass of two fish functional groups and to generate biomass distribution maps for the Atlantis ecosystem model. We also determined which seafloor and bathymetric variables were most important in GAM predictions. Here, depth was the most important variable in explaining the spatial patterns of encounter/nonencounter of the two functional groups and the spatial patterns of biomass of the MBC group, and slope was the second most important variable in explaining the spatial patterns of encounter/nonencounter of the MPL group. This result broadly agrees with that of a previous study from Hawai‘i, where depth and mean slope were found to be the most influential predictors of shallow coral reef species distribution (Costa et al., 2015) and depth the most influential predictor of deep-water snapper species distribution (Misa et al., 2013). These studies did not, however, consider substrate classes, and, here, we found that the hard-soft factor was as important as the confounding effects in explaining the spatial patterns of encounter/nonencounter of the MBC group and more important than the confounding effects in explaining the spatial patterns of biomass of the two functional groups. Regarding morphological attributes, eastness was more important than the

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Figure 37.6 Smoothed curve of the additive effect to the estimated (A and B) probability of encounter and (C) biomass of the mesophotic planktivore (MPL) group for the individual environmental parameters considered in the (A and B) binomial and (C) gamma generalized additive models (GAMs) of the functional group. Dotted lines represent 95% confidence intervals, and each mark along the x-axis a single observation. Note that the scale of the y-axis differs from one panel to the next for display purposes. RDMV, Relative distance to the mean (see the main text for a definition of this variable).

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Figure 37.7 Predicted biomass distribution maps for the Atlantis ecosystem model around Maui-Nui for (A) the mesophotic benthic carnivore (MBC) group and (B) the mesophotic planktivore (MPL) group.

636 Chapter 37 confounding effect of year in explaining the spatial patterns of biomass of the MBC group, and RDMV was more important than the confounding effect of monitoring program in explaining the spatial patterns of biomass of the MPL group. Collectively, results suggest that the MBC group is most frequently encountered on hard bottoms at depths ranging from 0 to 175 m where the seafloor exhibits relatively high slope, while the MPL group is most frequently encountered at depths ranging from 0 to 175 m. The biomass of the MBC group is greater in areas characterized by hard substrate in shallower depths, and where the slope is orientated towards the east, while the biomass of the MPL group is greater in topographically distinct (RDMV) areas. Slope (at 125 m spatial resolution) was also a good predictor of the probability of encountering both functional groups, but probably for different reasons. Slope is associated with hard substrate which is more suitable for benthic sessile communities to attach on and they provide habitat for invertebrates which are prey of the MBC group. Slope also results in an upward current delivering and enhancing plankton biomass (Gove et al., 2016) that is prey for the MPL group. Additionally, east-facing slopes will receive more currents due to the prevailing oceanographic conditions providing more prey for the benthic filter feeders (and indirectly to the MBC group) and planktivores (including the MPL group). Since most mesophotic planktivores are relatively small, geomorphically distinct areas could offer finer complexity (beyond the native resolution of the data) and shelter to avoid predation, explaining the relationship between biomass and RDMV found in this study. It was not immediately clear which version of the three hard-soft maps was most appropriate and which spatial resolution should be chosen for the seafloor metrics to predict the spatial patterns of encounter/nonencounter and biomass of the functional groups. For example, the 4-level hard-soft factor was influential in predicting encounter/nonencounter for the MBC group, while the 2-level hard-soft factor was influential in predicting MBC group biomass. Another example is for slope, which was retained in the GAMs when the 125 m resolution was considered, but not when the 25 or 625 m resolutions were considered. These results underline the importance of considering different definitions/thresholds of substrate character and different spatial resolutions for the morphological metrics when modeling the spatial distribution patterns of species and functional groups inhabiting shallow coral reef and seamount-based ecosystems.

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