Development of an Atlantis 

Model for Hawaiʻi to Support 

Ecosystem-based Management 

Mariska Weijerman

With contributions from: Kirsten Leong, Zack Oyafuso, Tomoko Acoba, 

Thomas Oliver, Benjamin Richards, Johanna Wren, Hannah Barkley, Kaitlyn 

Lowder, Stacie Robinson, Réka Domokos, Donald Kobayashi, Joey Lecky, 

and Jennifer Raynor

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Pacific Islands Fisheries Science Center

NOAA Technical Memorandum NMFS-PIFSC-113

https://doi.org/10.25923/cwqb-1z04

December 2020
ii

Development of an Atlantis Model for 

Hawaiʻi to Support Ecosystem-based 

Management 

Mariska Weijerman

With contributions from Kirsten Leong, Zack Oyafuso, Tomoko Acoba, Thomas Oliver, 

Benjamin Richards, Johanna Wren, Hannah Barkley, Kaitlyn Lowder, Stacie Robinson, 

Réka Domokos, Donald Kobayashi, Joey Lecky, and Jennifer Raynor

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

1845 Wasp Boulevard

Honolulu, HI 96818

NOAA Technical Memorandum NMFS-PIFSC-113

December 2020

U.S. Department of Commerce

Wilbur L. Ross, Jr., Secretary

National Oceanic and Atmospheric Administration

Neil A. Jacobs, Ph.D., Acting NOAA Administrator

National Marine Fisheries Service

Chris Oliver, Assistant Administrator for Fisheries 
iii

Recommended citation

Weijerman, M. 2020. Development of an Atlantis model for Hawaiʻi to support ecosystem-based 

management. U.S. Dept. of Commerce. NOAA Technical Memorandum NOAA-TM-NMFSPIFSC-113, 140 p. doi:10.25923/cwqb-1z04/TM-PIFSC-113. 

Copies of this report are available from

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration 

1845 Wasp Boulevard, Building #176

Honolulu, Hawaiʻi 96818

Or online at

https://repository.library.noaa.gov/
iv

Table of Contents

List of Tables ................................................................................................................................. vi

List of Figures ............................................................................................................................... vii

Executive Summary ....................................................................................................................... ix

Introduction..................................................................................................................................... 1

Step 1. Identified Management Objectives and Performance Indicators........................................ 2

Socio-economic objectives ......................................................................................................... 2

Biological Objectives.................................................................................................................. 4

Description of select indicators:.................................................................................................. 4

Step 2. Climate and Management Scenarios................................................................................... 5

Step 3. Model Initialization............................................................................................................. 6

3A. Study area: Insular ecosystems of the southeastern Hawaiian Islands ................................ 6

Physical Environment ............................................................................................................. 7

Biological Environment.......................................................................................................... 7

Human Dimensions................................................................................................................. 8

3B. Atlantis Ecosystem Model Development............................................................................. 9

Model geometry .................................................................................................................... 10

Physics submodel .................................................................................................................. 11

Hydrodynamic forcing .......................................................................................................... 12

Environmental drivers ........................................................................................................... 12

Biology submodel ................................................................................................................. 14

Harvest submodel.................................................................................................................. 31

Step 4. Model Calibration and Validation .................................................................................... 33

Calibration: No Fishing............................................................................................................. 33

Calibration: Incremental increase in fishing mortality ............................................................. 35

Validation: Historical fishing .................................................................................................... 36

Validation: Modeled trophic structure of the community ......................................................... 41

Step 5. Model Uncertainty ............................................................................................................ 42

Recycling of nutrients ............................................................................................................... 42

Groups with high trophic impacts ............................................................................................. 43

Biomass of meso- and subphotic group .................................................................................... 43

Parameters of pH and temperature effect size .......................................................................... 44

Best practice taking uncertainty into account ........................................................................... 45

Step 6. Model Application: Climate Change Impacts to Fisheries and Ecosystem Productivity . 47
v 

Step 7. Conclusions and Next Steps ............................................................................................. 49

Acknowledgements ....................................................................................................................... 51

Literature Cited ............................................................................................................................. 52

Appendix A: Characteristics of spatial areas in Atlantis .......................................................... 68

Appendix B: Meta-analysis of sensitivities to pH and temperature of Hawaiian organisms ... 71

Appendix C: Vertebrate species aggregated by functional groups ........................................... 75

Appendix D: Invertebrate data analysis .................................................................................... 83

Appendix E: Meso- and subphotic initial conditions ............................................................... 87

Appendix F: Biomass of shallow prey fish and mesopelagic scatter layer .............................. 93

Appendix G: Allocation of Hawaiian monk seal biomass per model box ................................ 95

Appendix H: Mixed trophic impact analyses ............................................................................ 99

Appendix I: Calibration: No Fishing ..................................................................................... 100

Appendix J: Validation with fixed mortality per gear type ................................................... 124
vi

List of Tables

Table 1. Functional groups included in the southeastern Hawaiian Islands Atlantis model. ....... 15

Table 2. Initial biomass values in metric tons (t) for all functional groups. ................................. 18

Table 3. Initial and after tuning (italics) rate parameters for invertebrate functional groups ....... 24

Table 4. Key life history parameters for vertebrate functional groups. ........................................ 26

Table 5. Summary of bottomfish reproductive seasons (compiled by C. Kelley of University of 

Hawaiʻi, Hawaiʻi Undersea Research Laboratory). .......................................................... 30

Table 6. Pedigree of model parameters. Values are confidence intervals of the values used in the 

model and reported under Step 3. NA = not available. ..................................................... 49
vii

List of Figures

Figure 1. Severe thermal-induced “bleaching” of Cauliflower corals (Pocillopora meandrina) 

along the Kona coast of Hawai’i. Photo courtesy of Bill Walsh DAR Kona. .................... 5

Figure 2. The eight Hawaiian islands of our study area with the inset figure showing the location 

of the Hawaiian Archipelago in the Pacific Ocean............................................................. 6

Figure 3. Schematic representation of the Atlantis model structure and input files. Submodels 

and files colored in green are obligatory, others are optional............................................. 9

Figure 4. Geometry of the southeastern Hawaiian Islands Atlantis model. .................................. 11

Figure 5. Biomass trajectories for 6 functional groups of a 100-year simulation with no external 

disturbances. The dashed green line is the calculated biomass at carrying capacity (i.e., 

B0) or initial biomass for reference. ................................................................................. 33

Figure 6. Numbers-at-age for 6 vertebrate functional groups of a 100-year simulation with no 

external disturbances. ........................................................................................................ 34

Figure 7. End to initial ratio of weight-at-age for 6 vertebrate functional groups of a 100-year 

simulation with no external disturbances. ......................................................................... 35

Figure 8. Catch equilibrium plots of targeted functional groups. Catch (blue line) is plotted 

against fishing mortality rates (F=0, 0.05, 0.10, 0.20, 0.40, 0.60). Black vertical line is the 

natural mortality added for comparison. ........................................................................... 36

Figure 9. Comparison of observed (red dots) and modeled (blue line) historical time series of 

catches per functional group. ............................................................................................ 37

Figure 10. Comparison of observed (red dots) historical time series of spearfish catches per 

functional groups and modeled (blue line) catches with a gear-specific fixed fishing 

mortality. ........................................................................................................................... 38

Figure 11. Comparison of modeled (Mod; red line) and observed (Obs; blue dots with 1SE) 

standardized biomass of fish functional groups. ............................................................... 39

Figure 12. Comparison of modeled (Mod: red line) and observed (Obs: blue dots with 1SE) 

standardized biomass of green sea turtles (GT), bottomfish (BFW), and monk seals (MS) 

functional groups. ............................................................................................................. 40

Figure 13. Comparison of modeled (Mod: red line) and observed (Obs: blue dots with 1SD) 

standardized cover of sessile benthic groups. ................................................................... 40

Figure 14. Relationship between the log biomass and size represented by the trophic level of the 

marine community at initial conditions. ........................................................................... 41

Figure 15. Ratio of end to start biomass of the large phytoplankton group based on a 5-year 

simulation run adding the decay function (decay-wc), including atmospheric deposits 

(include_atmosphere), three depths set as the deep mixing depth (mix_deep_depth) and 

the water column mixing coefficient (wc_kz). ................................................................. 43

Figure 16. Monte Carlo simulations of changing the initial biomass of 5 meso-and subphotic 

functional groups (MBP, MBC, MPL, SBC, SPL) with 30% showed very little change in 

biomass of bottomfish group (BFB). Values show resulting biomass in blue line and 1 SD 

in black bars. ..................................................................................................................... 44

Figure 17. Sensitivity to survival scalars of organisms' response to (A) adjusting calculation of 

the pH and temperature survival scalars and (B) increasing and decreasing these scalars 

by 10%. Codes of the functional groups on the vertical axis are explained in Table 1. ... 46

Figure 18. Standardized social-ecological effects of climate change compared to a base case 

scenario. The top panel shows the mean of the socioeconomic and ecological indicators. 
viii

In the bottom panel, the left-most four bars represent socio-economic indicators and the 

right four bars ecological indicators. The error bars are based on parameters reflecting the 

sensitivity of organisms’ survival scalar to changes in pH and temperature and show 

results of 10% increase and 10% decrease in these parameter estimates. ........................ 48
ix

Executive Summary

We developed an Atlantis model for the insular ecosystems around the inhabited Hawaiian

Islands (HI Atlantis). Key steps in this model development were to (1) summarize socioeconomic and ecological objectives from multiple stakeholders in combination with robust 

performance indicators; (2) select candidate management strategies; (3) initialize the model for 

which thousands of parameters were needed; (4) model calibration and validation with a hindcast 

simulation of fishery and environmental stressors; (5) model uncertainty; (6) model application; 

and (7) summarize the results and discuss next steps. Although the ultimate goal of the model is 

to evaluate effectiveness of alternative management strategies, in this report we focus on the 

steps involved to develop the HI Atlantis model. 

Most data sets for model initialization were obtained from 2010 to 2015. Largest uncertainties 

around parameter values were diet composition of especially non-commercial deep water 

species, spatial distribution of the mesopelagic scatter layer (i.e., myctophids), spatial 

distribution and habitat affinity of juvenile fish of most groups, spatial distribution of plankton 

community, and sensitivity of fishes and marine mammals to temperature increase and pH 

decrease. The model would also greatly improve from increased spatial resolution of the 

oceanographic fluxes; currently, the model uses the HYCOM data with a grid size of 0.25 

degrees (~4 km) and loops over the available, non-particular years 2010–2013. 

We successfully validated and calibrated the model. It can reproduce historic catches and 

biomass, and the modeled ecosystem conforms general ecological rules such as a trophic 

pyramid with few apex predators and a large base of primary producers and detritus consumers, 

and a linear relationship between log-biomass and size/trophic level. Sensitivity analyses showed 

that changes in biomass of some groups have a large impact on the biomass of other groups. 

These groups had a top-down (sharks) or a bottom-up control (large phyto- and zooplankton). 

This means that in future model applications, uncertainty around biomass and/or rate parameters 

of these groups should be taken into account to address this parameter uncertainty.

We applied the model to look at changes in socio-ecological indicators driven by projected 

climate change effects. Values of selected social indicators were similar between a simulation 

with climate change impacts and the base case (no climate change impacts), but the coral reef 

ecosystem structure will change with an increase in planktivores driven by an increase in phytoand zooplankton and a decrease in corals driven by pH and temperature-induced mortality. The

ecosystem productivity and resilience, as quantified by the selected indicators, showed a severely 

degraded system under the climate change scenario.
x 
1 

Introduction

Marine ecosystem modeling supports management that seeks to balance sustainable development 

and resource use with the conservation of biodiversity and functioning marine ecosystems. The 

overall goal of an end-to-end ecosystem model is the development of a simulation model in 

which all key biogeophysical and social-ecological dynamics are linked in order to address 

scientific and management questions and to evaluate the effectiveness of candidate management 

strategies so as to maintain or improve the supply of ecosystem goods and services we rely on 

from the ecosystems of interest. In this manner, ecosystem models can serve as decision-support 

tools, highlighting potential trade-offs among the different objectives of marine resource users. 

One such end-to-end model is the Atlantis model.

1 The success of Atlantis lies in its capacity to

strike a balance between realism and tractability. Ecosystems are created in Atlantis threedimensionally, using linked polygons that represent major geographical features of somewhat 

homogenous biogeophysical characteristics. Information is added on local oceanography, 

chemistry, geology and biology—such as currents, nutrients, plankton, invertebrates and fish—

and key ecological processes are simulated. Atlantis is currently one of the most comprehensive 

and up-to-date ecosystem models (Plagányi et al. 2007), and it has successfully been applied to 

ecosystems spanning from the polar region to the tropics and from Australia to Europe 

(Weijerman et al. 2016). The Atlantis framework is very suitable for management strategy 

evaluation as it incorporates sub-modules for each major step in the management cycle: the 

marine environment, the behavior of industry, fishery monitoring and assessment processes, and 

management actions and implementation (Fulton et al. 2007). Similar to the approach taken to 

conduct a management strategy evaluation (Kaplan et al. In review) is the approach to develop

an ecosystem model. One has to clearly define a set of management objectives and performance 

criteria related to achieving these objectives. Alternative management scenarios of interest to 

evaluate need to be identified and means to calculate the performance of each strategy under 

uncertainty. Lastly, one needs to quantify the (social-ecological) tradeoffs and communicate 

these to decision makers. Building on existing research (Weijerman et al. 2020), in this report we 

outline these steps in relation to HI Atlantis model development (Steps 1–5), we apply the model 

to explore climate change (Step 6), and we summarize results and discuss next steps (Step 7).

 

1 https://research.csiro.au/atlantis/
2 

Step 1. Identified Management Objectives and Performance Indicators

Contributors: Kirsten Leong and Zack Oyafuso

In the United States, the National Marine Fisheries Services has formulated a “Roadmap to 

Ecosystem-Based Fisheries Management” (EBFM; NOAA Fisheries, 2016) to explicitly account 

for social-ecological trade-offs due to environmental and management changes. An important 

aspect of EBFM is the synergy between management and science. Managers should 

communicate to the scientists the specific data and information needs they have and describe the 

form in which these products are most useful. In general, fisheries management has been focused 

on biological objectives while economic and especially social objectives are stated in very broad, 

non-specific terms (Benson and Stephenson 2018). However, for EBFM to be effective, a 

holistic approach is required that clearly links comprehensive (social and natural) scientific 

research and management objectives (Harvey et al. 2017). Implementation of EBFM has been 

slow in part because these objective settings are not always fully specified (Arkema et al. 2006; 

Link and Browman 2017). 

To address this gap, Weijerman et al. (2020) identified socio-economic objectives and indicators 

reflecting currently available data. Future work will identify crucial data gaps and 

recommendations for including important concepts that are not already measured and may 

improve future iterations of the HI Atlantis model. At a stakeholder workshop (Weijerman, 

Leong et al. 2019) we also identified additional objectives which were not suitable to include in 

the Ecopath with Ecosim modeling framework (Christensen and Walters 2004) used in 

Weijerman et al. (2020) but can be incorporated into the Atlantis framework. Ultimately, eight 

objectives specified by multiple agencies, and therefore deemed important for most stakeholders, 

were included with associated indicators. In some instances, existing data did not address the 

objective as fully stated in the documents, e.g., for the objective “equitable access” – we do not 

have data for equitable but do have data to address access. We noted these data gaps within each 

objective. 

Socio-economic objectives

1. Access to fishing grounds. 

• Number of closed areas: management scenarios for both coral reef and bottomfish 

fisheries include spatial closures which restrict spatial access to fisheries

• Number of fishing days: total allowable catch management is relevant to the 

bottomfish fishery. Number of fishing days measures the length of the fishing 

year, subjected to a total allowable catch that when met closes the fishery for the 

rest of the fishing year.

• Per-capita fishing opportunities/access to resources

 Data gap: none of these metrics address equitable access. Key locations for 

sustainable subsistence/livelihood and artisanal fishing have not been 

systematically identified with stakeholder input.
3 

2. Maintain intangible benefits. 

• Diving/snorkeling experience related to conditions of the marine environment 

• Frequency of recreational fishing (number of trips per year)

• Viewing option of charismatic megafauna

 Data gap: Data on intangible benefits (e.g., aesthetic, sense of place) is extremely 

limited, although we do have some data on forms of recreation (diving, 

recreational fishing, wildlife viewing).

3. Food provision. 

• Variability in fish catches (an indicator for stability of food provisioning)

• Catch allocated for home consumption

• Catch allocated for sharing in the community

• Biomass of culturally important species (e.g., bottomfish [snappers and a 

grouper], goatfishes [kumu], forage fish [opelu, akule], parrotfishes [uhu], 

unicornfishes [kala])

 Data gap: These indicators do not measure social cohesion and resilience; 

however, it recognizes the importance of fisheries as a means for providing social 

cohesion in family and community units through the sharing of the catch.

 Data gap: Identification of culturally important marine species through systematic 

and formal means, such as expert solicitation and literature review, is not 

available. Species of economic importance (sale $3 or higher) were used as an 

initial proxy for a subset of culturally important species. In addition, the identified 

indicators do not address nutritional security of vulnerable populations.

4. Coastal protection. 

• Coral cover fronting human coastal communities

 Data gap: Hard coral cover is presumed to bolster an area’s coastal resilience and 

therefore we use it as a proxy for a socio-economic indicator for coastal 

resilience. However, we do not have a relationship between coral cover and 

indicators related to the sensitivity of an area to negative coastal effects, such as, 

number of homes in flood zones, damage from king tides, coastal erosion rates.

5. Sustain marine revenue. 

• Revenue from fishing

• Trophic level of the fish catch

• Fishing expenditures

• Livelihood profile: number of jobs are related to the marine sector

• Number of commercial fishing licenses

• Number of registered Deep-7 bottomfish boats
4 

• Number of species groups sustainably harvested

Biological Objectives

6. Maintain productive ecosystems. 

• Biodiversity

• Trophic level of the community

• Total fish biomass 

• Biomass of sharks and other predators

7. Maintain resilient ecosystems. 

• Ratio of coral + crustose coralline algae (CCA) /turf and fleshy algae

• Ratio of demersal/planktivorous fish

• Herbivorous fish biomass

• Biomass of large (> 50 cm) parrotfishes

Description of select indicators:

• Fish biomass: total biomass of all fish groups, or of particular functional groups of 

interest (e.g., parrotfishes, piscivores) aggregated by Atlantis box level or an 

appropriate spatial scale

• Trophic level of the fish community: A high trophic level implies a lightly fished 

ecosystem or a system that fishes all trophic levels and not just top predators 

resulting in a fish community representing all ecological functions

• Diving/snorkeling experience scores are based on attributes of the marine 

environment considered important to divers/snorkelers or affecting their 

likelihood of diving/snorkeling in the future from a Hawaiʻi dive-expenditure 

survey (Weijerman et al. 2020)

• Total catch: total catch of all fish groups, or of particular functional groups of 

interest (e.g., parrotfishes, piscivores) aggregated at the Atlantis box level or other 

appropriate spatial scales

• Catch allocation: catch that is allocated for sale, home consumption, or sharing in 

the community

• Number of closed areas: number of closed areas as specified by the management 

scenario

• Number of fishing days: for scenarios with total catch limits, the number of days 

where the total catch limit is not met

• Ratio of coral and CCA/turf and fleshy algae: ratio of the total cover of CCA and 

coral divided by the total cover of turf and fleshy macroalgae

• Number of species groups sustainably harvested: with sustainably harvest we 

assume that the biomass of the species group > 40% of the initial biomass 

(Weijerman et al. 2018)
5 

Step 2. Climate and Management Scenarios

Reefs in Hawaiʻi are predicted to be directly threatened by climate change stressors over the next 

few decades. Hawai’i’s high-latitude reefs are likely to suffer soonest from ocean acidification 

(OA) stress, compared to other tropical reefs, and will experience annual bleaching from 

approximately 2035 onwards (van Hooidonk et al. 2016). Based on an EBFM workshop 

((Weijerman, Leong et al. 2019) numerous local management actions were identified, targeting 

governance, ecological state components, equitable access issues and social state components to 

mitigate the global impacts. In a parallel study, we developed an Ecopath with Ecosim model to 

explore the efficacy of alternative fisheries management scenarios to maintain or improve 

identified societal goals (Weijerman et al. 2020). Here, we explored the effects of projected 

climate change and compared model output with and without climate change impacts simulated

(see Step 6). 

Figure 1. Severe thermal-induced “bleaching” of Cauliflower corals (Pocillopora 

meandrina) along the Kona coast of Hawai’i. Photo courtesy of Bill Walsh DAR Kona.
6

Step 3. Model Initialization

To obtain an understanding of the ecosystem complexities we briefly describe the key 

biophysical features and the human uses of marine resources with respect to our study area 

(section 3A) and then detail the model development (section 3B). For a simplified overview of 

the model development, see the Story Map.

2 For general model structure and algorithms please 

review (Fulton et al. 2004; Fulton et al. 2011; Audzijonyte et al. 2019). For coral specific 

relationships (e.g., coral growth, coral-algal interactions, coral-sediment relationship, and 

structural complexity-predator avoidance relationships) please see Weijerman et al. (2015). 

Underlying the usefulness of model forecasts is the availability of pertinent data and expert 

opinion on the implementation of key ecosystem dynamics into more simplistic algorithms and 

identifying parameters for each of the biological groups and ecological dynamics, 

hydrodynamics and socio-economic dynamics. To obtain these data, partnerships were formed 

between management agencies and various science disciplines (University of Hawaiʻi at Mānoa, 

Hawaiʻi Institute of Marine Biology, Pacific Island Fisheries Science Center, Hawaiʻi Division 

of Aquatic Resources, Pacific Islands Regional Office, and Western Pacific Regional Fisheries 

Management Council).

3A. Study area: Insular ecosystems of the southeastern Hawaiian Islands

Our area of interest is the insular ecosystems surrounding the eight inhabited Hawaiian Islands 

located in the Pacific Ocean (Figure 2).

Figure 2. The eight Hawaiian islands of our study area with the inset figure showing the 

location of the Hawaiian Archipelago in the Pacific Ocean.

 

2 https://storymaps.arcgis.com/stories/c1898d0cb90745feb008e9c9daff8f39
7

Physical Environment

The Hawaiian Islands are located in the oligotrophic (nutrient poor) subtropical gyre of the North 

Pacific. The ocean is characterized by water temperatures of around 25°C at the surface (0–30 m) 

with temperatures of around 22°C at 50 m after which it gradually declines to 8°C at 400 m.3

Salinity and oxygen varied minimally in these 0–400 m water depths and are around 35 ppt and 

200 μmolO2/kg, respectively.4 Due to the clarity of the water, light penetrates to 200–400 m 

depth. Decadal 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). 

Open ocean water is the primary source water for coral reef systems. Once on the shallow reef, 

this source water can be dramatically altered by physical, chemical, and biological processes. 

The conditions of adjacent open ocean surface waters strongly impact coral reef ecosystem 

metabolism and health. For example, offshore biological productivity impacts delivery of 

inorganic and organic nutrients that serve as critical energy sources for biological processes 

within coral reef ecosystems. Upwelling of deep, cool waters via internal waves or topographic 

upwelling can provide additional nutrients as well as intermittent refuge to thermal stress in 

shallow coral reefs. Offshore circulation patterns influence larval dispersal and recruitment, 

which can enhance biodiversity and increase ecosystem resilience to anthropogenic stressors. 

The physical properties of the ocean water also influence the abundance of corals. Corals have a 

very narrow range of optimal conditions. For example, when the temperature rises for prolonged 

periods (4–8 weeks) above the mean summer maximum, the corals will expel their symbiotic 

algae that live in their tissue giving them a bleached look (Jokiel and Coles 1990). The polyps 

can still filter feed but prolonged absence of their symbiotic algae will often lead to coral 

mortality. Global acidification of open ocean waters results in the transport of progressively 

more acidified source water to coral reefs which leads to declining rates of calcification, 

increasing dissolution, and a shift in calcium carbonate reef budgets toward net dissolution 

(Langdon and Atkinson 2005). The current effects of climate change have a profound impact on 

the growth and mortality of corals and other calcifying organisms (Kroeker et al. 2013; Busch 

and McElhany 2016).

Biological Environment

The shallow (0–30 m) coral reefs ecosystems fringing the Hawaiian Islands are home to about 70 

species of scleractinia or hard corals (Veron and Stafford-Smith 2002) but only a handful 

dominate the benthos. These prominent structural corals include Porites lobata, Porites 

compressa, Pocillopora meandrina and Montipora spp. These coral colonies provide food and 

habitat for many invertebrate and fish species making it a very productive ecosystem despite the 

surrounding oligotrophic open ocean waters. Fish diversity and abundance is very high in coral 

reef ecosystems compared to other coastal ecosystems partly because of the protection provided 

by the structural complexity of reefs for juvenile and small fishes (Graham et al. 2006; Rogers et 

 

3 http://hahana.soest.Hawai’i.edu/hot/hot-dogs/ 4 http://hahana.soest.Hawai’i.edu/hot/hot-dogs/
8 

al. 2014). Corals compete for benthic space with, for example, algae, soft corals, tunicates, 

zooanthids and sponges.

The steep slopes and clear waters around the islands lead to distinct biological transitions 

between the highly productive, photic shelf areas and the more depauperate light-limited lower 

slopes (Struhsaker 1973; Kelley et al. 2006; Weijerman, Grüss, et al. 2019). The upper slope 

limits are at about 130–150 m and the lower depth limits extend beyond our model boundaries of 

400 m to 1000–4000 m. Ecosystems associated with mesophotic depths, which are characterized 

as located between 30 and 150 m (Kahng et al. 2014), still have scleractinian, zooxanthellate 

coral species present, with patchily distributed reef environments dominated by Leptoseris and 

Montipora spp. with increasing depth (Pyle et al. 2016). Calcareous algae are the dominant algal 

group between 30 and 150 m, particularly with extensive Halimeda spp. beds in the Maui Nui 

area (the islands of Moloka’i, Maui, Lana’i and Kaho’olawe). Large areas of the calcareous 

crustose rhodoliths also have been observed to depth of 170 m (Rooney et al. 2010). However, 

little remains known about the broader spatial distribution of these algal beds. 

At deeper, subphotic, depths which we characterize as located between 150 and 400 m, 

productivity is largely based on the organic “snow” from sloppy feeding and sinking planktonic 

particles (Siegel and Deuser 1997; Baldwin et al. 2018). Subphotic habitats are typically 

uniform, low-relief, hard bottom with patches of CCA at 160–180 m (Rooney et al. 2010), and at 

deeper depths the primary benthic taxa are azooxanthellate corals, soft corals, sea anemones, 

benthic ctenophores, feather stars and gorgonians (Kelley et al. 2006). The low habitat diversity 

has resulted in less affinity with habitat type for deep-water fishes compared to the shallow reef 

zone (Parrish 2006). Benthopelagic fish (i.e., fishes associated with benthic substrates) rely on 

currents to transport zooplankton into their foraging areas and hence greater concentrations of 

fish are found along features on the sea bottom where currents form (e.g., seamounts and cliffs). 

Changes in fish community structures are thought to be more influenced by top-down (predators, 

fishing) than bottom-up (productivity) controls (Parrish 2009; Weijerman et al. 2017). Clear 

faunal breaks in fish communities have been found in these deeper (150–400 m) waters with the 

largest break at 230 m and other breaks at 190 m and 310 m (Weijerman, Grüss, et al. 2019). 

The chlorophyll maximum is around 150 m (Cullen 1982) which leads to night-time vertically 

migrating zooplankton, squid and fishes from depths of around 400–800 m to the shallow (30–

200 m) depths, collectively known as the mesopelagic scatter layer (Benoit-Bird et al. 2001). 

Fish species include zooplanktivorous myctophids and gonostomatids, and carnivorous 

Bethosema spp, Polymixia spp., and Synagrops spp. (Struhsaker 1973). Marine mammals and 

large piscivorous fish species target these migrating species during their night-time feeding dives 

(Benoit-Bird and Au 2003). These vertical feeding migrations likely provide a means for the 

exchange of energy between the different depth zones (Steinberg et al. 2008; Drazen and Sutton 

2017; Gloeckler et al. 2018). 

Human Dimensions

Coastal communities engage with, and rely on, marine ecosystems; therefore, they face a 

complex set of interlocking challenges. Changes in marine ecosystems driven by global-scale 

shifts in ocean warming and acidification, as well as local human-induced disturbances, may be 

eroding the ability of these ecosystems to provide the goods and services on which humans rely. 

Less clear are the protocols to address and adapt to these declines. To respond intelligently to 
9 

these changes we must understand the complex interactions among the coastal environment, 

marine organisms, and human communities.

Coral reefs are economically productive, with estimates of the direct total economic value of 

Hawai’i’s reefs topping $360 million per year, or $9.7 billion over the next 50 years (Cesar and 

Van Beukering 2004). They also provide other critical ecosystem services, such as coastal 

protection (Storlazzi et al. 2019), food security (Grafeld et al. 2017), cultural benefits (Ingram et 

al. 2018), and strengthening social relations (Kittinger et al. 2015). Through a coupling of the 

biophysical components with the human use sector and tracking relevant indicators, it is possible 

to quantify the impacts of changes in the ecological state components on human well-being 

(social state components) and changes in the social state on the ecological well-being (Tam et al. 

2019). In Hawaiʻi the most important human well-being domains related to coral reef ecosystem 

viability are the following (Breslow et al. 2016; Leong et al. 2019): Culture and Spirituality; 

Social Relations; Health; Economic/Material well-being; and Safety and Security. Indicator 

development and data collection for each of these human domains and the ecosystem services 

related to those domains are in progress. However, for some indicators existing data sets are 

present to operationalize the relationships between human and ecological well-being and to 

develop a robust understanding of how ocean and management changes affect the reliance on 

and engagement of human communities on the coral reef ecosystems (Weijerman et al. 2020).

3B. Atlantis Ecosystem Model Development

The structure of Atlantis consists of various submodels that can be as detailed as the user desires 

(Figure 3).

Figure 3. Schematic representation of the Atlantis model structure and input files. 

Submodels and files colored in green are obligatory, others are optional.
10

We will discuss each of these building blocks in relation to the development of the Hawaiʻi 

model and explain where parameter values came from for our study area. 

Model geometry

Contributors: Tomoko Acoba, Thomas Oliver, Benjamin Richards

HI Atlantis covers approximately 8905 km2 from 0 to 400 m deep (Figure 4). The design of the 

spatial structure involved placing boundaries around areas with similar biogeographic and 

oceanographic conditions as well as aligning boundaries with those of fishery reporting zones. 

The geography was categorized by hard/soft substrate information and bathymetry. Selected 

depth contours at 30 m, 150 m, and 400 m were derived from the synthesis of the bathymetry 

available from the University of Hawaiʻi (UH), Hawaiʻi Mapping Research Group (HMRG). 

Biological components (benthic cover composition and fish communities) were obtained from 

the PIFSC Reef Assessment Monitoring Program survey data5 for the 0–30 m shallow areas. 

These resulted in 28 spatial areas (boxes) with (somewhat) homogenous fish and benthic

composition for the shallow (<30 m) spatial areas. 

For the deeper (30–400 m depth) areas, we used a hierarchical clustering technique to identify 

spatial blocks with similar oceanographic characteristics. Variables used for clustering were 

satellite-derived 2003–2015 monthly means of chlorophyll-a (MODIS-Aqua), photosynthetically 

active radiation (PAR), a proxy for turbidity (K490), and sea surface temperature (SST). We 

clustered these variables based on Euclidian distance and joining linkages based on outliers 

(complete-linkage). By changing the number of required clusters we identified distinct areas 

around each of the islands that were robust (same clusters) under these changes which resulted in 

twenty-seven 30–150 m boxes and nineteen 150–400 m boxes (Weijerman 2017). 

For all spatial areas, habitat characterization was based on hard/soft substrate information 

derived from two sources: (1) benthic habitat maps generated from satellite images analysis for 

the 0–20 m depth range ((Battista et al. 2007) and (2) backscatter data collected during multibeam surveys for the 30–400 m depth range (UH, HMRG). The major structures of the benthic 

habitat maps derived from the satellite images were re-categorized into hard and soft substrate 

classes. Backscatter data reflected the hardness of the substrate as values between 0 (soft, e.g., 

sand) and 255 (hard, e.g., basalt) for each 400 x 400 m pixel. We classified pixels with values ≥ 

110 as hard. In ArcGIS we used the tool ‘zonal statistics’ to aggregate the pixels for each of the 

spatial Atlantis areas and calculated the percentage hard and soft.

Islands are connected by four boxes (81–84 in Fig. 4) to allow for movement between islands. 

Around the entire model domain we created six ‘boundary boxes,’ which allow for the advection 

of plankton (including larvae) and nutrients, but which have no dynamic processes. Each spatial 

area was further divided into vertical water column layers and one sediment layer. For the 

inshore boxes (0–30 m) we assume complete mixing and they only have one layer (0–30 m = 

‘shallow’), the 0–150 m boxes have two layers (‘shallow’ and ‘mesophotic’), and the 0–400 m 

and deeper boxes have three layers (‘shallow’, ‘mesophotic’, and ‘subphotic’). Boundaries of 

boxes close to the delineation of the Hawai`i Division of Aquatic Resources (DAR) fishery 

 

5 https://www.pifsc.noaa.gov/cred/pacific_ramp.php
11

reporting zones, were adjusted to align with the fishery grids to facilitate the allocation of catch 

time series in the Atlantis harvest submodel. Once the Atlantis boxes were finished, small 

adjustments were made to ensure that the modeled area matched as closely as possible (<2% 

difference) to the true area (Appendix A). The total 0–400 m area included 13% shallow habitat 

(1129 km2), 44% mesophotic habitat (3890 km2) and 44% subphotic habitat (3886 km2).

Figure 4. Geometry of the southeastern Hawaiian Islands Atlantis model.

The outer boxes (0, 7, 14, 15, 51, and 80) are non-dynamic “boundary,” and boxes 85–92 are island boxes 

with no dynamics; others are dynamic boxes with closest to shore, 0–30 m depth and 1 vertical layer, 

adjacent to those are 0–150 m with 2 vertical layers; and farther offshore are 0–400 m boxes with 3 

vertical layers. Four boxes between the islands (81–84) allow for interisland movements. 

Physics submodel

Most of the physical parameters in Atlantis were not changed. For example, the functional 

response curve for bioirrigation and bioturbation activity were left as parameterized for the 

original southeast Australia model. We adjusted some parameters based on sensitivity analysis 

(see under Step 5 “Recycling of Nutrients”). 
12

Hydrodynamic forcing

Contributor: Johanna Wren

Physical exchanges (advection and diffusion) between Atlantis boxes were calculated from the 

regional HYbrid Coordinate Ocean Model (HYCOM) for the Hawaiian Islands Region with KPP 

mixed layer provided by Dr. Yanli Jia at the International Pacific Research Center, University of 

Hawaiʻi at Mānoa.6 The regional HYCOM model has a spatial domain spanning 166–150°W and 

16–26°N and consists of daily snapshots with 1

/25-degree (~4 km) horizontal resolution and 32 

depth layers. We used the non-particular years 2010–2013 (2009 was the first year, but only has 

data for half a year, 2014 had an extreme recruitment pulse, 2015 and 2016 had abnormally 

warm waters) as our baseline and looped over these years for the duration of a baseline 

simulation. The potential temperature, salinity and vertical velocity variables were re-gridded to 

an evenly spaced grid using bilinear interpolation, then disaggregated to increase the spatial 

resolution by a factor of 3 for better coverage inside the coastal Atlantis polygons. We averaged 

the temperature and salinity for each Atlantis box and water column layer. Vertical flux was 

calculated for each box and layer by averaging the vertical velocity and multiplying it by the area 

of the box and the depth of the layer. Horizontal fluxes over the sides of each of the Atlantis 

boxes were calculated by defining an area around each side of each polygon and calculating the 

mean horizontal current speed and direction inside each area for each depth layer. Additionally, 

we calculated the relative angle between the side of each polygon and the flow direction and 

multiplied the mean current speed by the cosine of the relative angle to account for nonperpendicular flow. The resulting current speed was multiplied by the surface area of the 

polygon’s side to obtain a flux across the side in m3s-1. 

Initial conditions for physical, biological and chemical oceanographic data came from in-situ 

observations and literature (Table 2). For the shallow boxes, we used primarily observational 

data from NOAA PIFSC Reef Assessment Monitoring Program surveys.

7 For the offshore 

spatial areas (depth >30 m), light attenuation, temperature, salinity, oxygen, nutrients, and 

chlorophyll-a data were downloaded from the Hawaiʻi Ocean Time-Series database8 using the 

bottle-extracted data. For nitrogen in the upper (0–200 m) water column, we used the results of 

the “High Precision, High Sensitivity Method” since the concentrations are so low in the waters 

around Hawaiʻi that other methods do not properly measure them.

Environmental drivers

Ocean acidification

Contributor: Hannah Barkley

Surface tropical seawaters are generally supersaturated with respect to the carbonate minerals 

(e.g., calcite, aragonite, and high-magnesium calcites) from which marine organisms construct 

their shells and frameworks. At deeper water depths, seawater becomes undersaturated and these 

minerals begin to dissolve, imparting an important control (amongst other factors) on the 

distribution of coral reefs. The effects of ocean acidification on calcification rate appears not to 

 

6 Available for download at: http://apdrc.soest.Hawai’i.edu/datadoc/hycom_Hawai’i_0.04_kpp.php. 7 https://www.pifsc.noaa.gov/cred/pacific_ramp.php

8 http://hahana.soest.Hawai’i.edu/hot/hot_jgofs.html
13

be directly related to changes in pH, per se, but instead to corresponding changes in the degree to 

which seawater is supersaturated with respect to the carbonate minerals (e.g., aragonite) 

(Langdon and Atkinson 2005). A change in carbonate ion concentration results in a proportional 

change in Ωarg such that as ocean acidification continues, the surface ocean Ωarg-values will 

decline. As the saturation state declines, it is harder for marine calcifiers to precipitate the 

calcium carbonate they need to build their skeletons. By the year 2065, rates could decline 60 ± 

20% relative to preindustrial levels. 

Projected changes in the aragonite saturation were included as force files. The variables sea 

surface temperature (SST), surface salinity, surface pressure of CO2 and pH were obtained from 

fully coupled models in the Coupled Model Intercomparison Project 5 (CMIP5)

9

. All model 

outputs were re-gridded to the same 1×1° regular grid using bilinear interpolation. If multiple 

runs were available for a model, these runs were averaged first. Based on these data, a multi 

model ensemble was created. Missing values over land were filled in using a Poisson grid filter 

in the zonal direction. Values where sea ice was present in the GFDL-ESM2M_rcp85 model 

were masked out. Monthly values were averaged to obtain yearly or decadal averages. Aragonite 

saturation state was calculated from SST, surface pressure of CO2, pH, and salinity.

Ocean warming

Contributor: Johanna Wren

Projected changes in ocean temperature (SST) also came from CMIP5 using just the AR 8.5 

scenario. All 22 model outputs were used to calculate the average SST over the inhabited 

Hawaiian Islands (161°W–154.5°W and 18.5°N–22.5°N) spatial area. We first subtracted the 

historic average (1970–2000) from the future projection to obtain the projected change for each 

model and then took the average across all 22 models. To keep the spatial variation as 

determined by the hydrodynamic model, we added (or subtracted) the projected change to the 

last year of the hydrodynamic model (2013). We also included the historical peaks in 

temperature in 2014–2015 and 2018 that led to severe coral bleaching in Hawaiʻi (Sale et al. 

2019).

Sensitivity of marine organisms to environmental changes

Contributor: Kaitlyn Lowder

An increasing number of laboratory and field studies provide insight into the sensitivity of a 

broad range of marine organisms by replicating future increases in ocean temperature and 

changes to carbonate chemistry (decreases in pH and aragonite saturation). We applied a metaanalytic framework to estimate sensitivity of organisms from tropical Pacific or Indo-Pacific 

marine waters, encompassing bacteria, algae, invertebrates, and fishes (Appendix B). From an 

initial screening of 1,543 full papers, we identified 334 papers with controlled experiments that 

exposed organisms to either changes in pH or temperature. For each experiment, we extracted 

metadata on a broad range of variables, including species, life stage, treatment level, and 

experimental duration, as well as response data that ranged from survival and growth to 

predation susceptibility and nutritional quality. Response values and sample sizes from 

 

9 http://pcmdi9.llnl.gov/ esgf-web-fe/
14

individual experiments were converted to effect size (Hedges’ g or odds ratio, depending on the 

data type). For temperature, intercept values from intercept-only models were converted to an 

approximation of Q10, a coefficient that reflects the measure of the rate of change in a response 

as temperature increases by 10 °C. Responses to pH were fit as intercept-only, linear, or 

quadratic relationships of effect size with pH decreases up to 1 unit. All effect sizes were 

standardized to the most sensitive organism and these resulting ‘survival’ scalars were

incorporated into Atlantis as a scalar on growth-related parameters (Appendix B).

Land-based sources of pollution

Contributor: Joey Lecky

Coastal areas are impacted by river and groundwater flows carrying pollutants, nutrients, and 

sediments to nearshore waters. Sediment and nutrient data of these daily loads came from the 

Ocean Tipping Point (OTP) project10 and were aggregated for each coastal Atlantis box in a 

forcing file. These data represent current (2010–2015) sediment and nutrient inputs into coastal 

waters. To get seasonal patterns we downloaded monthly climatological rainfall data (1981–

2010) from 75 coastal stations in the inhabited Hawaiian Islands11 and took the mean rainfall for 

each Atlantis box. We assumed that there was a linear relationship between rainfall and the 

sediment loads and estimated the monthly sediment input. Sediments were modeled as the 

nitrogen in labile detritus (20%) and refractory detritus (80%) by converting carbon to nitrogen. 

We further assumed that the OTP sediment load contained 1% nutrients from fertilizer consisting 

of N–NO3. Since OTP nutrient inputs were from Onsite Sewage Disposal Sites (OSDS, e.g., 

cesspools) only, we assumed that those inputs were constant over time and consisted of N-NH4. 

Biology submodel

Multispecies functional groups 

Inclusion of species in the model was dependent on a minimal time span of 10 months spent in 

the model domain, especially with regard to its energy cycle (feeding, excretion, and 

reproduction). For example, humpback whales spend a part of their time in the nearshore 

Hawaiian waters; however, they hardly feed here and, therefore, are assumed not to be a great 

contributor to the energetic pathways of the insular ecosystem and are excluded from the model. 

In contrast, some spinner and bottlenose dolphin pods that are resident in Hawaiʻi defecate in 

shallow bays and feed in deeper waters on vertically migrating myctophid fishes, squids, and

shrimps. Therefore, they and their diet components are included in the model. Generally, species 

were aggregated into functional groups based on similar life history, diet, and habitat, with finer 

taxonomic resolution for species of special concern (conservation, fishery). We defined 59 

functional groups (Table 1) including marine mammals (3 groups), reptiles (2), reef fish (9), 

meso- and subphotic fish (5), commercially important deep-water fish (3), invertivorous rays (1), 

coastal pelagics (4), micronekton (2), zooplankton (4), macrobenthic invertebrates (5), 

meiobenthos (2), filter feeding sessile invertebrates (3), structural benthos (6), primary producers 

(5), bacteria (2) and detritus (3). Species composition of vertebrate functional groups is provided 

in Appendix C. 

 

10 http://www.pacioos.Hawai’i.edu/projects/oceantippingpoints/#datasources 11 http:// www.ncdc.noaa.gov/cdo-web/datatools/normals 
15

Table 1. Functional groups included in the southeastern Hawaiian Islands Atlantis model.

Category Code No

Functional group name 

(with examples of main families/species)

Marine mammals MS

SPD

BND

1

2 

3 

Monk seals

Spinner dolphins

Bottlenose dolphins

Sea turtles GT

HT

4

5

Green sea turtle

Hawksbill turtles

Reef fish RCO

RBR 

RGR

RPA 

RBC 

RLP 

RSP 

ROF 

RBP

6

7

8 

9 

10

11

12

13

14

Corallivores (butterflyfish)

Herbivore browsers (chub, unicornfish)

Herbivore grazers (surgeonfish)

Parrotfishes

Benthic carnivores (snapper, goatfish, squirrelfish)

Large planktivores (unicornfish, soldierfish)

Small planktivores (sergeant, chromis, cardinalfish)

Other reef fish

Benthic piscivores (grouper, moray eel, scorpionfish)

Meso and sub 

benthic piscivores MBP 15 Benthopelagic piscivores (eel, scorpionfish, trumpetfish)

Mesophotic fish MBC

MPL

16

17

Benthic carnivores (wrasse, goatfish, flounder, perch)

Planktivores (anthias, flame wrasse, triggerfish)

Subphotic fish SBC

SPL

18

19

Benthic carnivores (flounder, beard fish, orange rakefish) 

Planktivores (spikefish, boarfish, armorhead)

Bottomfish BFW

BFB 

UKU

20

21

22

Bottomfish in water column: Opakapaka, Onaga, 

Kaleakale, Lehi

Bottomfish close to substrate: Ehu, Hapu’upu’u, Gindai

Uku

Invertivorous rays IVR 23 Invertivorous rays (sting ray)

Coastal pelagics PIS 

SHR

SHP

RAY

24

25

26

27

Roving piscivores (jack, barracuda)

Sharks (reef and pelagic shark)

Mackerel scad, bigeye scad

Manta ray

Mesopelagic scatter 

layer

MSL

CEP

28

29

Planktivorous micronekton (myctophid, gonostomatid)

Squid

Mobile inverts LOB

BC 

BD 

BG 

OCT

30

31

32

33

34

Large crabs, lobster, heterocarpus

Benthic carnivores (sea star, mollusk, crustacean)

Benthic detritivores (amphipod, isopod, sea cucumber, 

crab, brittle star)

Benthic Grazers (urchin, snail, sea hare)

Octopus, 

Meiobenthos

BM 

INF

35

36

Small inverts (small shrimp, crab, mollusk, foraminifera)

Infauna (burrowing inverts e.g., polychaete, nematod)

Filter feeders BFF

BIV 

DFF

37

38

39

Shallow filter feeders (sponge, tunicate, octocoral, 

zoanthid, sand dollar, bryozoan)

Bivalve, clam, oyster, pen shell

Deep filter feeders (deep water coral, anemone, benthic 

ctenophore, feather star, gorgonian)
16

Category Code No

Functional group name 

(with examples of main families/species)

Structural benthic 

species

PRM

PRB 

MON

POC

LEP

CCA

40

41

42

43

44

45

Porites massive

Porites branching

Montipora

Pocillopora

Leptoseris

CCA

Primary producers MAF

MAC

TRF 

PL

 

PS

46

47

48

49

 

50

Macroalgae fleshy

Macroalgae calcareous

Turf algae

Large phytoplankton (diatom, Trichodesmium, 

dinoflagellate, diazotroph)

Small phytoplankton (cyanos (prochlorococcus, 

synechococcus), picoeukaryote, coccolithophorid)

Micronekton ZS

ZM

ZL

ZG 

51

52

53

54

Small zooplankton (ciliate, protozoa, nanoflagellate)

Meso zooplankton (copepod, pteropod, polychaete)

Large zooplankton (chaetognath, Stomadidae, larvae, 

decapod shrimp, amphipod, ostracod, euphausiid)

Gelatinous zooplankton (jellies, salp, siphonophore, 

pyrosome, ctenophore, hydrozoan, pelagic tunicate)

Nutrient cyclers PB

BB

55

56

Pelagic bacteria

Benthic bacteria

Detritus DL

DR

DC

57

58

59

Labile detritus

Refractory detritus

Carrion detritus

Initial conditions: Biomass and spatial distribution

The currency of Atlantis is nitrogen so we converted all biomass values to nitrogen based on the 

Redfield ratio of C:N being 5.7 and a dry-weight-to-wet-weight ratio of 20%. These ratios are 

hard-wired into the code so could not be changed. The vertebrates can have up to 10 age classes, 

with a minimum 1 year represented in each class. Age-classes of vertebrate groups were further 

divided into structural (skeleton) and reserve (soft tissues) weights based on the reserve to

structural weight ratio of 2.65. Invertebrates are divided into juveniles and adults. Atlantis does 

not include larval stages; recruits in Atlantis are represented by the youngest individuals settling 

from their pelagic larval stage into their (benthic) juvenile habitat.

For the <30 m boxes, biomass and horizontal distribution of chlorophyll-a (phytoplankton), 

benthic sessile organism (corals, algae) and reef fish came from PIFSC Reef Assessment 

Monitoring Program surveys.

12 Site-level data were pooled up to Atlantis boxes for each 

functional group (Table 2). Mobile crypto invertebrate data came from the Autonomous Reef 

Monitoring Units (ARMS) program (Appendix D). Recovered species were grouped in the 

functional group based on their main diet components and we estimated their biomass. Biomass 

for the octopus and lobster functional groups for the shallow boxes (for deep boxes see below) 

were derived from commercial fishery data where we assumed a sustainable fishery effort of 0.2 

 

12 https://www.pifsc.noaa.gov/cred/pacific_ramp.php
17

per year and a ratio of commercial to recreational (un-reported) catch of 1:5 and that 2⁄3 of the 

reported catch came from the 0–30 m area. 

For the meso- and subphotic depth layers, phytoplankton data came from the Hawaiʻi Ocean 

Time-Series database.

13 Zooplankton and squid biomass and vertical distribution data were 

obtained from literature (Steinberg et al. 2008; Choy et al. 2016). The obtained biomass values 

per unit of volume of each box and layer was summed and divided by the total biomass to get a 

relative value for each box which was used as the horizontal distribution. Benthic sessile 

invertebrate data were obtained from PIFSC surveys, HURL data (Appendix E) and Veazey et al. 

(Veazey et al. 2016). The deep filter feeder (DFF), octopus (OCT), lobster and large crab (LOB), 

and bivalves (BIV) functional group data were obtained directly from HURL data (Appendix E) 

in which abundance was estimated for sessile and mobile invertebrates. For the DFF group we 

assumed that 1 organism occupies 0.1 m2 to convert abundance to cover. These abundance 

estimates were multiplied with average weight for the functional species group, i.e., 375 g for 

DFF (Wilkinson and Cheshire 1990), 750 g for OCT (Van Heukelem 1976), 500 g for LOB 

(assumed), and 10 g for BIV (assumed). The bivalve biomass was further multiplied by 100 and 

the OCT and LOB biomasses by 10 to account for unnoticed species. Spatial allocation of the 

meso- and subphotic DFF, OCT and BIV groups was based on the percentage of hard substrate 

derived for the seaflor of the model domain (Dove et al. 2019). As the LOB functional group 

combines lobsters, which favor hard substrate, and large crabs, which favor soft sediments, this 

functional group was assumed to be homogeneous across the modeled area. Biomass of benthic 

detritivores and benthic carnivores was based on the same calculations as the cryptofauna 

(Appendix D) as these organisms can be quite small and hard to detect in the submarine surveys. 

For these calculations, where we did not have in situ complexity estimates, we used the 

percentage hard substrate as a complexity indication with >80% hard being equivalent as 

complexity 4, between 60% and 80% as complexity level 3, between 40% and 60% as 

complexity 2 and below 40% complexity 1.

Fish abundance came from meso- and subphotic surveys conducted by PIFSC (Asher et al. 2017, 

PIFSC unpublished data), Struhsaker (Struhsaker 1973) and the University of Hawaiʻi at Mānoa

(UH) Drazen lab (Moore et al. 2016) and Hawaiʻi Underwater Research Lab (HURL) and was 

converted to biomass (Appendix E). 

Spatial distribution for the meso- and subphotic species was based on species-specific affinities 

with seafloor characteristics and depth (Dove et al. 2019). Each functional group had slightly 

different variables that explained the deviance between observed and predicted encounter or 

biomass data (Weijerman, Grüss, et al. 2019). 

Vertical diurnal distribution for plankton came from Steinberg et al., (2008) and for all other 

groups from fishBase, literature and expert opinion.

 

13 http://hahana.soest.Hawai’i.edu/hot/hot_jgofs.html
18

Biomass estimates and spatial distribution of “Shallow prey fish” and the “Mesopelagic Scatter 

layer” 

Contributors: Réka Domokos and Donald Kobayashi

The fish functional groups “shallow prey fish” and “mesopelagic scatter layer” play a crucial role 

in the trophodynamics and shallow prey fish are also an important component in fish landings. 

However, observations from available surveys only had occasional sightings of these groups and 

therefore it was not possible to make predictions based on habitat associations (Weijerman, 

Grüss, et al. 2019). We conducted a thorough review of available published and gray literature to 

estimate their biomass and spatial distribution (Appendix F). 

Biomass and spatial distribution for protected species

Contributor: Stacie Robinson

Data on marine mammals and sea turtles were provided by the PIFSC Protected Species 

Program14 and literature (Piacenza et al. 2016). Sea turtles were assumed to be equally 

distributed among the boxes to 150 m (shallow and mesophotic areas) and spend most of their 

time in the shallower habitats (Dr. T. Todd Jones pers. comm.). Dolphins spend the day in 

shallow to mesophotic depth ranges whereas at night they forage in mostly deeper waters (Dr. 

Erin Oleson pers. comm.). We allocated their distribution accordingly, assuming they spend 

25%:30%:45% of their time (24 hours) in respectively shallow, meso- and subphotic habitats. 

For monk seals, tagging data provided reliable estimates of distribution among the Atlantis boxes 

and depth dive profiles for vertical distribution (Appendix G). 

Table 2. Initial biomass values in metric tons (t) for all functional groups.

Group Biomass 

(t)

Source Assumption

Small 

phytoplankton

324,438 PIFSC surveys unpublished data, 

HOTDOGSa website

Coastal areas represented by 

PIFSC RAMPb surveys; 

offshore areas represented by 

station Aloha

Large 

phytoplankton

124,216c

(629,400)

PIFSC surveys unpublished data, 

HOTDOGS website

Coastal areas represented by 

PIFSC RAMP surveys; offshore 

areas represented by station 

Aloha

Microzooplan

kton

173,201 HOTDOGS website Coastal areas represented by 

PIFSC RAMP surveys; offshore 

areas represented by station 

Aloha

Meso

zooplankton

390,857

(276,706)

Choy et al. 2016, Steinberg et al. 

2008

Coastal areas represented by 

PIFSC RAMP surveys; offshore 

 

14 www.pifsc.noaa.gov/psd/
19

Group Biomass 

(t)

Source Assumption

areas represented by station 

Aloha

Large 

zooplankton

1,140,208I 

(232,442)

Choy et al. 2016, Steinberg et al. 

2008

Same as (Choy et al. 2016)

decapod and other crustaceans 

groups

Gelatinous 

zooplankton

140,173 Choy et al. 2016, Steinberg et al. 

2008

Same as (Choy et al. 2016)

filter-feeding and carnivorous 

gelatinous groups

Corals 395,330 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php

Biomass = 126 g/m2 (Ruiz 

Sebastián and McClanahan 

2013)

Leptoseris 36,443 (Veazey et al. 2016), PIFSC 

unpublished mesophotic surveys

Assume same biomass as 

shallow water corals: 126 g/m2

Turf algae 328,578 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php

Assumed to be 1% at 

mesophotic depths. Biomass = 

250 g/m2 (Smith et al. 2001)

conversion factors from 

(Atkinson and Grigg 1984)

Calcareous 

macroalgae 

(MA), 

CCA 

238,992

132,269

https://www.pifsc.noaa.gov/cred/

pacific_ramp.php, PIFSC 

mesophotic surveys

Substrate suitability is same as 

for Leptoseris at mesophotic 

depths with 1.25*Leptoseris

cover based on PIFSC surveys 

and 0.5 for CCA. Biomass = 

140 g/m2 (Smith et al. 2001)

conversion factors from 

(Atkinson and Grigg 1984)

Fleshy MA 240,239 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php, PIFSC 

mesophotic surveys

Substrate suitability is same as 

for Leptoseris at mesophotic 

depths with 1*Leptoseris cover 

based on PIFSC surveys. 

Biomass = 447 g/m2 (Smith et 

al. 2001) conversion factors 

from (Atkinson and Grigg 

1984)

Shallow filter 

feeders

1,071 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php

Biomass =375 g/m2 (Wilkinson 

and Cheshire 1990)

Deep filter 

feeders

68,689 HURL Biomass =375 g/m2 * density * 

% hard substrate. Cover is 

biomass/375

Bivalves 8,356 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php, HURL

ARMS results multiplied by 1.5 

to account for undercounting in 

shallow water; for meso and 

subphotic zones HURL 

densities * 100 to account for 
20

Group Biomass 

(t)

Source Assumption

undercounting and a biomass of 

10 g per bivalve

Squid 169,200 (Choy et al. 2016) Same as Choy et al. (2016) epiand mesophotic mollusks 

groups

Octopus 4,836 Based on commercial fishery 

data

Fishery is sustainable at F=0.2. 

Recreational fishery is 5* 

commercial fishery yield.

Biomass = total Yield * F

Infauna 

(polychaetes) 

3,181,629 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php

ARMS results multiplied by 10 

to account for uncounted spp. 

for shallow; for meso and 

subphotic zones assume same 

density as in Horne et al. 2010

Meiofauna 383,274 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php

ARMS results for shallow 

water; for meso and subphotic 

zones assume same density as 

in (Horne et al. 2010)

Benthic 

carnivores

58,958 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php

ARMS results for shallow 

water; for meso and subphotic 

zones assume same density as 

in (Horne, 2010)

Benthic 

detritivores 

158,391 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php

ARMS results multiplied by 1.5 

to account for larger 

detritivores (e.g., sea 

cucumbers)

Benthic 

grazers

177,560 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php

ARMS results multiplied by 1.5 

to account for larger grazers 

(e.g., urchins)

Large crabs

and and

lobster

19,717 PIFSC Stock Assessment Fishery is sustainable at F=0.2. 

Recreational fishery is 3 * 

commercial fishery yield.

Biomass = total Yield * F

Reef 

corallivores

341 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php SPC surveys

Reef 

herbivore 

browsers

1,939 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php SPC surveys
21

Group Biomass 

(t)

Source Assumption

Reef 

herbivore 

grazers

7,376 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php SPC surveys

Reef 

Parrotfish

2,071 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php SPC surveys

Reef benthic 

piscivores

1,572 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php SPC surveys

Reef benthic 

carnivores

4,147 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php SPC surveys

Reef large 

planktivores 

1,100 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php SPC surveys

Reef small 

planktivores

833 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php SPC surveys

Reef other 

fish

3,434 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php SPC surveys

Meso benthic 

carnivores

5,043 HURL, University of Hawaiʻi 

Drazen Lab, Struhsaker 1973, 

PIFSC ESD BRUV surveys

See Appendix E

Meso 

planktivores 

3,382 HURL, University of Hawaiʻi 

Drazen Lab, Struhsaker 1973, 

PIFSC ESD BRUV surveys

See Appendix E

Meso and sub 

benthic 

piscivores

2,159 HURL, University of Hawaiʻi 

Drazen Lab, Struhsaker 1973, 

PIFSC ESD BRUV surveys

See Appendix E

Sub benthic 

carnivores

948 HURL, University of Hawaiʻi 

Drazen Lab, Struhsaker 1973, 

PIFSC

See Appendix E

Sub 

planktivores

1,297 HURL, University of Hawaiʻi 

Drazen Lab, Struhsaker 1973, 

PIFSC

See Appendix E

Shallow prey 

fish

481 Calculations by Don Kobayashi 

Appendix F 

see Appendix F for assumptions 

made

Mesopelagic 

scatter layer

466,340

(46,634)

Calculations by Reka Domokos

Appendix F 

We divided the original value 

by 10 assuming they are 

spatially clustered - see section 
22

Group Biomass 

(t)

Source Assumption

“Validating/Sanity check of 

biomass calculations” 

Bottomfish in 

water column

3,770 PIFSC Stock Assessment Total biomass =1.3 ×

exploitable biomass from Stock 

Assessment

Bottomfish 

near substrate

826 PIFSC Stock Assessment Total biomass =1.3 ×

exploitable biomass from Stock 

Assessment

Uku 758 PIFSC Stock Assessment (Nadon et al. 2020)

Roving 

piscivores

4,129 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php towed-diver 

surveys, meso and subphotic 

surveys

Sharks 1,523 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php towed-diver 

surveys, meso and subphotic 

surveys

Invertivorous 

rays

3,473 (Weijerman, Grüss, et al. 2019)

Manta ray 585 https://www.pifsc.noaa.gov/cred/

pacific_ramp.php towed-diver 

surveys, meso and subphotic 

surveys

Monk seals 39 PIFSC Monk Seal Program See Appendix G for details

Spinner 

dolphins

101 PIFSC Cetacean Program; 

www.fisheries.noaa.gov/pr/speci

es/mammals/dolphins/spinnerdolphin.html

Bottlenose 

dolphins

214 PIFSC Cetacean Program; 

www.nmfs.noaa.gov/pr/species/

mammals/dolphins/bottlenosedolphin.html

Green sea 

turtle

1545 PIFSC Marine Turtle Program; 

(Chaloupka and Balazs 2007)

Hawksbill 

turtles

153 PIFSC Marine Turtle Program Assumption that Hawksbill 

population is about 10% of 

Green turtle population
23

Group Biomass 

(t)

Source Assumption

a

HOTDOGS = Hawaiʻi Ocean Time-series Data Organization and Graphical System; details at 

http://hahana.soest.Hawai’i.edu/hot/hot-dogs/interface.html. 

b

RAMP = Reef Assessment and Monitoring Program, SPC = stationary point count surveys; details at 

https://www.pifsc.noaa.gov/cred/survey_methods.php. 

c

Values in italics were changed during tuning, new values are in parentheses.

Validating/Sanity-check of biomass calculations

To validate the assumptions made in the estimations of the biomass values, diet and rate 

parameters of quite a few functional groups, we developed an Ecopath with Ecosim (EwE) 

model. Validation consisted of meeting two criteria: (1) mass balancing the EwE model; and (2) 

under a status quo simulation with EwE (i.e., no forcings such as fishing or climate change), all 

biomass values stay stable (Weijerman et al. 2020). We had to adjust the biomass of some groups 

by <10%. These changes were also incorporated in the final values of Atlantis. The only group 

we had to drastically change the initial biomass for (from the estimated 120 g/m2 to 4 g/m2) was 

the mesopelagic scatter layer (Appendix F). This is the group that resides at greater depth during 

the day and migrates up at night. As spatial data is unavailable, the estimate was based on the 

assumption that this group was equally distributed around the islands. However, if they occur in 

clusters it could be that the surveys were conducted in high-concentration areas, hence, assuming 

a skewed distribution could be appropriate. But since we do not have the data to make these 

spatial allocations we kept the distribution homogeneous and divided the estimated biomass by 

10. We also had to change the biomass of some of the plankton groups. Even though the EwE 

model was mass balanced with the initial values from the literature, the actual amount of 

zooplankton far exceeded the amount of phytoplankton leading to a depletion of food for 

zooplankton. We decreased zooplankton (ZM and ZL) and increased large phytoplankton (PL) 

making sure the model still mass balanced in EwE and conformed tropho-dynamics rules (Link 

2010). In EwE, diet also needed to be adjusted but since Atlantis uses the diet matrix as an 

availability matrix and other factors determine the actual consumption (e.g., occupying the same 

space at the right time and fitting in a predator’s mouth) we left the original values and checked 

the realized diet in Atlantis to make sure it corresponded with what is known from the literature.

Life History Parameters

Dynamic rate parameters were obtained from surveys, literature or expert opinion (Table 3, 

Table 4). For each functional group we used weighted means of species-specific life history data 

for all parameters where possible. During tuning, some of these values had to be adjusted.

For turtles, monk seals, spinner, and bottlenose dolphins we included a non-predation mortality 

to represent mortalities from entanglement in marine debris or other human-related causes not 

specifically represented by the model. On average 1 spinner and 0.5 bottlenose dolphins die in 

the Hawaiian Islands each year from anthropogenic causes. Bottlenose dolphins and monk seals 

also interact with bottomfish and shore-based hook-and-line fisheries, respectively, and can steal 

bait/catches from the hooks. 
24

Table 3. Initial and after tuning (italics) rate parameters for invertebrate functional groups

Group

Growth 

rate 

(MUM)

(per day)

Consump

tion rate 

(per day)

Mortality

ml - mQ Source

Small 

phytoplankton

0.013

(0.13)

NA 0 0 (Weijerman et al. 2013; Choy et al. 2016)

Large 

phytoplankton

0.014

(0.14)

NA 0 0 (Weijerman et al. 2013; Choy et al. 2016)

Microzooplankton 0.139 0.040

(0.010)

1E-07 0 (Choy et al. 2016)

Mesozooplankton0.079 0.0099

(0.0150)

0 0 (Choy et al. 2016)

Large 

zooplankton

0.052 0.0164

(0.054)

0 0 (Choy et al. 2016) other crustaceans

Gelatinous 

zooplankton

0.062 0.0077 0 0 (Choy et al. 2016) filter-feeding and 

carnivorous gelatinous

Squid 0.0265 0.0033

(0.0015)

0 0 (Choy et al. 2016)

Massive Porites 0.00016

(0.0032)

0.0011 0 0 (Richmond and Hunter 1990; Weijerman et 

al. 2013)

Branching 

Porites

0.00027

(0.0054)

0.0011 0 0 (Richmond and Hunter 1990; Weijerman et 

al. 2013)

Montipora 0.00044

(0.0088)

0.0011 0 0 (Cox and Ward 2002; Kolinski and Cox 

2003; Weijerman et al. 2013)

Pocillopora 0.00030

(0.0060)

0.0011 0 0 (Kolinski and Cox 2003; Weijerman et al. 

2013)

Leptoseris 0.000050

(0.00027)

0.0011 0 0 Assumption

CCA 0.0054 NA 0 0 (Weijerman et al. 2013)

Calcareous 

macroalgae 

0.0059 NA 0 0 (Weijerman et al. 2013)

Fleshy 

macroalgae

0.0060 NA 0 0 (Weijerman et al. 2013)

Turf algae 0.081 NA 0 0 (Weijerman et al. 2013)

Shallow benthic 

filter feeders

0.029

(0.050)

0.0037 0 0 See Appendix D

Deep benthic 

filter feeder

0.0106 0.0013 0 0 See Appendix D

Bivalves 0.0170 0.0021 0 0 See Appendix D

Benthic infauna 0.0263 0.0033 0 0 See Appendix D

Meiofauna 0.0277 0.0035 0 0 See Appendix D

Benthic 

detritivores

0.0424

(0.020)

0.0053 0 1E-07 See Appendix D

Benthic 

carnivores

0.0298

(0.010)

0.0037 0 1E-06 See Appendix D
25

Group

Growth 

rate 

(MUM)

(per day)

Consump

tion rate 

(per day)

Mortality

ml - mQ Source

Benthic grazers 0.0225 

(0.040)

0.0028 0 0 See Appendix D

Lobster 0.0391 0.0049

(0.0025)

0 0 See Appendix D

Octopus 0.0210 0.0026 0 0 See Appendix D
26

Table 4. Key life history parameters for vertebrate functional groups.

Group k

Infinite 

length

(cm)

Max. age 

(year)

Age at 

maturit

y (year)

Larval 

duration

(days)

Natural 

Mortality (M) Source

Reef 

corallivores

1.50 17.35 10.0 0.60 40 LN(0.01)/-

max_age = 0.97

(MacDonald 1981; Tricas 1985; Berumen 2005)

Reef 

herbivore 

browsers

0.23 53.8 27.7 3.31 70 LN(0.01)/-

max_age = 0.38

(Wilson and McCormick 1999; Nadon et al. 2015)

Reef 

herbivore 

grazers

0.8 32.6 27.1 2.12 60 LN(0.01)/-

max_age = 0.20

(Thresher et al. 1989; Wilson and McCormick 1999; Choat and 

Robertson 2002; Longenecker and Langston 2008; Nadon et al. 

2015)

Reef 

Parrotfish

0.73 46.2 15.1 1.70 35 0.30 (Ishihara and Tachihara 2011; Taylor 2012; Nadon et al. 2015)

Reef benthic 

piscivores

0.10 79.3 24.0 3.86 60 LN(0.01)/-

max_age = 0.23

(Nadon et al. 2015)

Reef benthic 

carnivores

0.41 43.2 10.2 2.82 23 LN(0.01)/-

max_age = 0.58

(Wilson and McCormick 1999; Nadon et al. 2015)

Reef large 

planktivores 

0.29 42.3 31.6 5.86 55 LN(0.01)/-

max_age = 0.15

(Wilson and McCormick 1999; Longenecker and Langston 2008; 

Nadon et al. 2015)

Reef small 

planktivores

0.52 11.8 4.9 0.82 29 LN(0.01)/-

max_age = 0.93

(Kohn and Helfrich 1957; Thresher et al. 1989; Fowler 1990; 

Hawn et al. 2005)

Reef other 

fish

0.48 32.3 8.1 1.86 121 LN(0.01)/-

max_age = 0.65

www.fishbase.org; B. Taylor pers. comm.
27

Group k

Infinite 

length

(cm)

Max. age 

(year)

Age at 

maturit

y (year)

Larval 

duration

(days)

Natural 

Mortality (M) Source

Meso benthic 

carnivores

0.30 36.9 6.5 1.9 40 LN(0.01)/-

max_age = 0.70

www.fishbase.org and assumptions based on ecological rules

Meso 

planktivores 

0.20 34.5 9.1 2.54 91 0.91 www.fishbase.org and assumptions based on ecological rules

Meso and

sub benthic 

piscivores

0.1 174.5 LN(0.01)/

-M = 0.24

4.1 50 0.19 www.fishbase.org and assumptions based on ecological rules

Sub benthic 

carnivores

0.25 28.2 LN(0.01)/

-M = 8.2

3.5 50 0.56 www.fishbase.org e and assumptions based on ecological rules

Sub 

planktivores

0.30 26.2 5 2.7 50 LN(0.01)/-

max_age = 0.92

www.fishbase.org and assumptions based on ecological rules

Shallow prey 

fish

1.21 31.9 3.48 1.33 120 LN(0.01)/-

max_age =1.48

(Kawamoto 1973; Clarke and Privitera 1995; Weng and Sibert 

2004; Welch et al. 2013)

Mesopelagic 

scatter layer

0.80 7.5 5 0.4 50 LN(0.01)/-

max_age = 0.92

www.fishbase.org and assumptions based on ecological rules

Bottomfish 

in water 

column

0.23 68.4 43.5 4.09 75 LN(0.01)/-

max_age = 0.11

(DeMartini et al. 1994; Moffitt and Parrish 1996; Andrews et al. 

2012; Luers et al. 2018) A. Andrews, pers comm

Bottomfish 

near 

substrate

0.27 57.6 47.2 5.07 75 LN(0.01)/-

max_age = 0.11

(DeMartini et al. 1994; Moffitt and Parrish 1996; Andrews et al. 

2012; Luers et al. 2018) A. Andrews, pers comm

Uku 0.14 81.0 32 4.3 120 0.10 (Nadon et al. 2020)
28

Group k

Infinite 

length

(cm)

Max. age 

(year)

Age at 

maturit

y (year)

Larval 

duration

(days)

Natural 

Mortality (M) Source

Roving 

piscivores

0.22 142.7 LN

(0.01)/-

M=12.4

3.21 120 0.37 (Sudekum et al. 1991; Nadon et al. 2015)

Sharks 0.08 368.2 32.4 4.3 360 LN(0.01)/-

max_age = 0.14

(Struhsaker 1973; Cortés 2000)

Invertivorous 

Ray

0.03 257.9 28 8.3 360 0.07 (Schluessel 2008; Schluessel et al. 2010; Dale and Holland 2012); 

www.fishbase.org

Manta Ray 0.1 796.0 LN(0.01)/

-M = 34.8

4 360 0.13 www.fishbase.org and assumptions based on ecological rules

Monk Seals 0.39 230 30 5 315 LN(0.01)/-

max_age = 0.15

(Winship et al. 2001)

Spinner 

Dolphins

0.59 220 20 7 335 LN(0.01)/-

max_age = 0.15

(Carretta et al. 2019); expert opinion

Bottlenose 

Dolphins

0.59 385 50 11 365 LN(0.01)/-

max_age = 0.09

(Carretta et al. 2019); expert opinion

Green Sea 

Turtle

0.09 106 70 25 365 LN(0.01)/-

max_age = 0.07

(Piacenza et al. 2016), T.T. Jones pers. comm.

Hawksbill 

Turtles

0.09 100 40 20 365 LN(0.01)/-

max_age = 0.12

(Van Houtan et al. 2016), T.T. Jones pers. comm.
29

Predator-prey interactions

Diet data for reef fish came from literature (Humphreys and Kramer 1984; Sudekum et al. 1991; 

Bruggemann et al. 1994; Meyer et al. 2001; Chen 2002; Choat et al. 2002; Glynn 2004; Kulbicki 

et al. 2005; Papastamatiou et al. 2015; Kelly et al. 2016; Nalley 2020), and fishBase. For pelagic 

functional groups (zooplankton, micronekton) diet data was derived from literature (Bernal et al. 

2015; Choy et al. 2016; Choy et al. 2017; Gloeckler et al. 2018). Little is known about the diet of 

subphotic species but they are assumed to be more generalist although niche partitioning occurs 

(Hopkins and Gartner 1992); since food is scarce most species have adapted to be able to eat a 

wide source of prey items depending on their location in the water column (benthic, 

benthopelagic) and forage behavior (hunter, ambush predator) (Gartner Jr et al. 1997; Drazen 

and Sutton 2017). Bottomfish diet is better documented (Haight et al. 1993; DeMartini et al. 

1996; Seki and Callahan 1988; B. Schumacher unpubl data; C. Kelley unpubl data; J. Drazen 

unpubl data). Dolphin diet came from (Trites and Pauly 1998; Benoit-Bird 2004; Bearzi 2005)

and diet data for monk seals came from isotope and fecal analyses (Appendix G). In general, 

hawksbill turtles eat predominantly sponges whereas green turtles eat algae. However, since 

sponges are limited in Hawai’i, the diet of hawksbill is almost identical to that of green turtles 

(T.T. Jones, NOAA-PIFSC pers. comm). Invertebrate diet came from an unpublished PIFSC 

database for the small invertebrates and from literature for the macroinvertebrates (Moriarty 

1982; Choy 1986; Stimson et al. 2007; Bell 2008) and suspension feeders (Bak et al. 1999; 

Houlbrèque et al. 2004; Rossi et al. 2004).

The Atlantis model uses an availability matrix where the fraction of prey available to predators is 

defined. Actual consumption depends on temporal (day/night) and spatial overlap. Further 

restrictions to consumption are based on the gape size of the predator if it eats age-structured 

prey and on whether a prey can hide in the substrate and hence become less available. Predatory 

fishes show significant correlations in both their maximum and median prey size with body size 

and also with gape size (Dunic and Baum 2017). Gape size is a fraction of how large the prey 

can be compared to the body size of the predator. 

For all functional groups except filter feeders we assumed a Holling Type II functional response. 

For filter feeders we assume a Holling Type I functional response (Houlbreque et al., 2004). 

Other ecological dynamics

Movement — Since seasonal variation in temperature is small around the inhabited Hawaiian

islands, we assumed there was no seasonal movement for any group as was shown for lobsters 

(O’Malley and Walsh 2013). Some species migrate for a short period to spawning grounds but 

we did not explicitly include these spawning migrations. 

Mortality — Mortality in Atlantis is mostly determined through the predator-prey interaction. 

However, to capture other mortalities, mortality can be applied as linear (e.g., disease, old age) 

or quadratic (density-dependent) and is used to tune the model to avoid extinctions and obtain 

reasonable estimates of modeled abundance in comparison to observed values. Atlantis also has 

the option to include mortality related to starvation, oxygen limitation, or ocean acidification. For 

the HI Atlantis model, we used 0.0001 d-1 for starvation mortality and have not used the oxygen 

limitation mortality. For simulations including ocean acidification we included a response curve 

from a meta-analysis (see the “Environmental drivers” section and Appendix B). 
30

Spawning period — Reef fishes spawn monthly around the full moon with peak spawning in the 

summer months. To simplify this, we only included the summer months. Spawning patterns for 

bottomfish, uku, and jacks were compiled by C. Kelley from various literature sources (unpubl. 

report; Table 5). Dolphins, monk seals, sharks, rays, and turtles were assumed to have offspring

in the summer months.

Table 5. Summary of bottomfish reproductive seasons (compiled by C. Kelley of 

University of Hawaiʻi, Hawaiʻi Undersea Research Laboratory).

Species J F M A M J J A S O N D

E. quernus

Caranx ignobilis

C. lugubris ? ? ? ? ? ? ? ?

P. cheilio ? ?

Seriola dumerili

A. rutilans ? ? ? ? ? ?

Aprion. virescens

E. carbunculus

E. coruscans

Lutjanus kasmira

P. auricilla

P. filamentosus

P. sieboldii

P. zonatus ? ? ? ? ?

Recruitment — There are several options for recruitment in Atlantis. We defined a BevertonHolt relationship for fish species. For the recruitment of mammals, sharks, rays, and sea turtles, 

each adult is assumed to have a fixed number of pups/calves. Recruitment and recruitment 

success of most stocks are uncertain and difficult to estimate, therefore these are tuned in the 

model to achieve realistic abundance levels for the different stocks, and to a lesser degree are 

based on literature values. 

Structural complexity — Rugosity was visually estimated by PIFSC-CRED survey divers 

whereby approximately each 25 cm height increase resulted in a point higher in the rugosity 
31

score with values bounded between 1 (flat) and 5 (highly complex). Atlantis distinguished 

geomorphological structures that can provide complexity (such as the boulders around Ni’ihau) 

and structural complexity from bioengineers (corals and crustose coralline algae). Since the 

rugosity scores were an aggregated value, we accounted for coral cover and subtracted the 

following amounts from the rugosity values; coral cover < 10% resulted in a subtraction of 0.5; 

coral cover between 10 and 15% a subtraction of 1, coral cover between 15% and 20% a 

subtraction of 1.5 and if coral cover was >20% we subtracted 2. In the model, rugosity increases 

with coral growth and decreases with coral erosion or coral predation. We included erosion by 

boring urchins (BG) and benthic carnivores (BC) and coral predation by parrotfishes (RPA). 

Corallivorous butterflyfishes just eat the coral polyps and were assumed not to contribute to 

bioerosion. We also included a relationship between complexity (or rugosity) and prey 

availability whereby prey becomes less available to predators when complexity increases (Bozec 

et al. 2015). 

Harvest submodel

Atlantis allows for different ways to include fishing. For example, you can force fishing by a 

functional group specific fishing mortality, total catches per functional group, total catches per 

functional group per fishery fleet and/or gear type or by (dynamic) effort. In HI Atlantis, we 

included reef fish fishery as catches by functional group by spatial area by gear type (spear, net, 

line, other). The bottomfish fishery was divided into a commercial and recreational fleet and both 

were included as catches by functional group by spatial area by year.

Historic commercial landings

Contributor: Zack Oyafuso and Jennifer Raynor

Hawaiʻi DAR commercial data reported from 1995 to 2017 was requested from the West Pacific 

Fisheries Information Network15 by species, area, gear type and year. We received 128,761 data 

records including pelagic species and unknown species. About 1,200 records, making up 20.9 t

in weight had no associated species and were deleted from further analysis. For 406 observations 

(7.5 t of weight), only the common (Hawaiian) name was given and we included an assumed 

species name. For example, “wana” is the Hawaiian word for urchin so we included the 

collector’s urchin as species-specific record as these were mostly encountered in the catch data. 

The remaining records all included a scientific species name, which we assigned to functional 

groups. All pelagic species (mostly tuna) and records where both numbers and weight were zero 

also were excluded leaving us with 94,659 usable records. We corrected two apparent data entry 

errors, one each for Caranx lugubris and Coris flavovittata. In each case, we divided the reported 

value by 10. Two-hundred-eighteen species had numbers caught but no associated weight. For 

the 28 fish species we used the mean length from fish surveys or the common length from 

fishBase and calculated the mean weight. For the invertebrates we assumed their weight was 5 g 

for small mollusks and crabs and 10 g for larger invertebrates. We then multiplied the mean 

weight by the numbers caught. All species were allocated to the appropriate functional group and 

we computed the annual catch per year per fishery reporting zone per functional group.

 

15 https://www.pifsc.noaa.gov › wpacfin
32

In ArcGIS, we allocated each fishery reporting zone to the corresponding Atlantis box. The 

coastal reporting zones are a fixed distance from shore so in some cases it represented the 0–150 

m and in others 0–400 m (or deeper) depth zone. Depending on the species we allocated the 

catches to the reef fish, mesophotic or subphotic groups. As catches are reported per trip it is 

possible that, for example, reef fish species are reported from a reporting zone offshore if that 

was the main area of fishing, so we inspected each record and allocated them to the appropriate 

Atlantis box based on the depth strata of that species. For functional groups that span the entire 

0–400 m depth (e.g., sharks, invertebrates) or just the top 150 m (uku, piscivores), we allocated 

50% of the catches as reef fish catches, 30% as mesophotic piscivore catches and 20% as 

subphotic piscivore catches and 60% reef fish and 40% mesophotic fish, respectively. For 

invertebrates we used the relative catch composition per gear type. For example, 95% of urchins

(functional group benthic grazers) were handpicked so we assumed they were all handpicked 

from shore in the shallow areas. Squid (functional group CEP) was caught for 25% by inshore 

handline which can extend to 150 m, 38% by “iki-shibi” which operates in waters >30 m and 

20% by bottom/deep-sea handline and we assumed that was also in waters 150–400 m and 12% 

by whipping. So we allocated the catches as 50% in 30–150 m and 50% in 150–400 m of the 

Atlantis areas closest to the reporting zones. Finally, we allocated the invertebrates caught by 

shoreline/gear types and all reef fish species to the reef fishery and all others (bottomfish, suband mesophotic fish and invertebrates caught by deep set gear types) to the bottomfish fishery.

Historic recreational landings

Contributor: Zack Oyafuso

Since commercial landings are poor reflections of the actual total catches for reef fish 

(Weijerman et al. 2013; McCoy et al. 2018), we used landings data from the Ocean Tipping 

Point (Wedding et al. 2017) for the recreational coral reef fish landings. Total recreational

landings for the tth year, sth coral reef functional group, bth Atlantis Box and gth gear type (CCtttttttt) 

were calculated using data sets from the Marine Recreational Information Program (Williams 

and Ma 2013; McCoy et al. 2018). Reliable time series data on total recreational landings are 

only available on an archipelagic scale from 2004 to 2015 (CCtttt⋅⋅). Compositional catch data were 

available across islands (index i) and gears for each functional group (pp(ii, gg|ss)) and are assumed 

to be constant across years. Recreational reef catch for the tth year, sth coral reef functional group, 

i

th island and gth gear type (CCtttttttt) was calculated as: CCtttttttt = CCtttt⋅⋅ pp(ii, gg|ss). Lastly, compositional 

data across Atlantis boxes within islands for each gear (assumed constant across years) were 

used to disaggregate island-level catch to Atlantis box-level catch, calculated as: CCtttttttt =

CCtttttttt pp(bb|ii, gg). For the years between 1995–2003 and 2016–2017, not covered by the 

recreational dataset, values were imputed by bootstrapping values from 2004 to 2015. For 

bottomfish recreational landings we used a 1:1 ratio (commercial to recreational landings) as an 

approximation of the unreported recreational catch used by the PIFSC stock assessment group 

(Langseth et al. 2018). For other subphotic and mesophotic fishes and all invertebrates we 

assumed a 1:1 commercial to recreational catch ratio.
33

Step 4. Model Calibration and Validation

To calibrate the model we followed best practices identified in Pethybridge et al. (2019) and 

simulated: (1) no fishing and (2) fishing with incremental increases. To validate the model we 

forced it with historical fishing and ocean changes (i.e., a hindcast simulation) and assessed 

model skill (Stow et al. 2009; Olsen et al. 2016). We also compared the modeled trophic 

structure with other tropical reef systems. 

Calibration: No Fishing

We first tuned the model in a control simulation (no fishing or other external stressors) to make 

sure no group became extinct or had biomass values of >5 times initial biomass after a 100-year 

simulation (Figure 5, Appendix I). We also checked that numbers-at-age and weight-at-age 

stayed between 0.5 and 1.5 times the initial conditions and were as expected (i.e., decrease in 

numbers from juveniles to older age classes; Figures 6 and 7, Appendix I). Lastly, we checked 

the realized diet from the model output with the known diet from the literature. 

Figure 5. Biomass trajectories for 6 functional groups of a 100-year simulation with no 

external disturbances. The dashed green line is the calculated biomass at carrying 

capacity (i.e., B0) or initial biomass for reference. 

Biomass trajectories of other functional groups are in Appendix I. N=Nitrogen, SP=spinner, 

B=bottlenose, G=green, H=hawksbill. R=Reef.
34

Dolphins decreased but stabilized as did the turtle groups to a lesser extend (Figure 5). It could 

be that our initial biomass was too high for these groups. Groups that were exploited by the 

fishery recovered and reached a new equilibrium as can be expected when released from fishing 

pressure (Appendix I).

Figure 6. Numbers-at-age for 6 vertebrate functional groups of a 100-year simulation with 

no external disturbances. 

Rainbow colors represent the age classes with red being the youngest and violet the oldest age class. 

Numbers-at-age of other vertebrate functional groups are in Appendix I.
35

Figure 7. End to initial ratio of weight-at-age for 6 vertebrate functional groups of a 100-

year simulation with no external disturbances.

Rainbow colors represent the age classes with red being the youngest and violet the oldest age class. 

Weight-at-age of other vertebrate functional groups are in Appendix I.

Calibration: Incremental increase in fishing mortality

To check productivity of the groups, we forced the model with incremental increases in fishing 

mortality (mFC) and checked the catches with equilibrium curves (Figure 8). We ran the model 

for 30 years to reach stability and saved the results of the last 5 years of the catches. We 

increased annual fishing mortality rate to 0.05, 0.1, 0.2, 0.4, and lastly 0.5. Using the mean of the 

last 5 years of catches of each simulation, we plotted the catch equilibrium values to make sure 

that with increases in fishing mortality (on x-axis), the catches increase (y-axis) until they reach a 

maximum sustainable yield (top of the curve) after which catches decrease as the remaining 

standing spawning stock declines below a critical size.
36

Figure 8. Catch equilibrium plots of targeted functional groups. Catch (blue line) is 

plotted against fishing mortality rates (F=0, 0.05, 0.10, 0.20, 0.40, 0.60). Black vertical line 

is the natural mortality added for comparison.

X-axis is fishing mortality rate per year, y-axis is catch in tonnes. Catches are the mean of the last 5 years 

of a 30-year simulation.

Validation: Historical fishing

Most of our data sources, represent 2010–2015 conditions. To account for the harvest of targeted 

species, we started the model in 1985 and let the model run for 10 years without any harvest to 

let exploited groups recover assuming that their biomass represents 1995 conditions. We forced 

the model with historical catch time series from 1995 to 2017 (including recreational as well as 

commercial catches) and compared the observed total catches with the modeled total catches. 

Modeled catches corresponded well with observed catch time series but biomass of some groups 

kept increasing so we added ‘unreported catches’ for those groups (Weijerman et al. 2015), 
37

which resulted in higher modeled catches compared to the catch time series for some groups 

such as grazers (RGR), parrotfishes (RPA) and benthic carnivores (RBC, ROF) (Figure 9). 

Figure 9. Comparison of observed (red dots) and modeled (blue line) historical time 

series of catches per functional group.

Codes represent functional groups explained in Table 1. Y-axis shows the biomass of the catch in tons.

We then explored the model’s ability to mimic catches per gear type using a fixed fishing 

mortality rate (mFC). We used the first 5 years of catch and biomass to estimate the daily fishing 

mortality mFC according to 1-exp (-F/365) whereby F the instantaneous fishing mortality 

calculated as: -LN (1-mu) and mu is the discrete exploitation rate: Catch/Biomass. Then tuned 

the mFC values until modeled catches were similar to observed catches (Figure 10). We 

validated model skill by using six complementary model skill assessment methods (Appendix J; 

Stow et al. 2009; Olsen et al. 2016). Overall model skill assessments were reasonable for the 

groups we did not change the catches for (non-target groups and invertebrates) and naturally not 
38

as good for the groups we increased the catches for, such as grazers (RGR), parrotfishes (RPA), 

and goatfishes/snappers/squirrelfishes (RBC).

Figure 10. Comparison of observed (red dots) historical time series of spearfish catches 

per functional groups and modeled (blue line) catches with a gear-specific fixed fishing 

mortality. 

Comparison plots for other gear types appear in Appendix J. 

As we are interested to see if the model can mimic the trends in biomass, we standardized the 

observational and modeled data to the maximum value per functional group trends (Figure 11). 

Sharks, uku, and shallow prey fishes are a bit higher in the model compared to observational data

but this could be because the observational data is just from shallow (<30 m) reef surveys while 

the model takes into account the total biomass across all depth ranges where the groups occur. 

Also, reef surveys do not include soft bottom where (juvenile) prey fish are commonly 

encountered. All other groups showed a good overlap. 
39

Figure 11. Comparison of modeled (Mod; red line) and observed (Obs; blue dots with 

1SE) standardized biomass of fish functional groups.

To validate the modeled green turtle and monk seal biomass, we used visual observation count 

data of nesting females on French Frigate Shoals for the turtles and monk seal counts around the 

main Hawaiian Islands. Again, since we are interested in the trend we standardized these counts 

to the maximum and compared that to the standardized modeled biomass of green turtles and 

monk seals (Figure 12). For bottomfish we had CPUE-derived relative biomass data of the 

exploited bottomfish groups. We standardized those values and since ōpakapaka (Pristipomoides 

filamentosus) is the most common species caught, we assumed this trend to be similar to the 

BFW modeled group (Figure 12). However, because we allowed exploited groups to recover 

from fishing by not implementing any catches in the first 10 years, the bottomfishes increased in 

biomass and when fishing started in 1995 decreased (Figure 12). The tail of the modeled biomass 

shows a stable biomass which corresponds fairly well with the CPUE–derived biomass (blue 

dots).
40

Figure 12. Comparison of modeled (Mod: red line) and observed (Obs: blue dots with 

1SE) standardized biomass of green sea turtles (GT), bottomfish (BFW), and monk seals 

(MS) functional groups.

We also compared the modeled and observed sessile benthic groups (Figure 13).

Figure 13. Comparison of modeled (Mod: red line) and observed (Obs: blue dots with 

1SD) standardized cover of sessile benthic groups.
41

Validation: Modeled trophic structure of the community

General ecological rules dictate that the biomass of lower trophic levels is higher than the 

biomass of higher trophic levels, i.e., the Lindeman trophic pyramid with apex predators at the 

top and primary producers and detritus eaters at the bottom. The positive correlation between 

trophic level and size in fish (Jennings et al. 2007) enabled using trophic level as an estimate of 

size. Previous studies have shown strong linear relationships between log-biomass and trophic 

level (Kolding et al. 2016). To look if this relationships held true for our modeled system, we 

aggregated the biomass of our groups in five trophic level (TL) bins: 2–2.49, 2.5–2.99, 3–3.49, 

3.5–3.99, and 4 or higher. Conforming to ecological rules we see a clear decrease in biomass 

with an increase in size (or TL; Figure 14). 

Figure 14. Relationship between the log biomass and size represented by the trophic 

level of the marine community at initial conditions.
42

Step 5. Model Uncertainty

Uncertainties in model output can originate from multiple sources, e.g., parameter settings, initial 

conditions (parameter uncertainty), simplifications of ecological processes (structural 

uncertainty), and the application of fisheries. Given the complexity and computational costs of 

running Atlantis, we need to know which groups are most sensitive to parameter uncertainty so 

that we can address these sensitivities in future scenario simulations by bounding the parameters 

of those groups between a possible minimum and maximum value and getting an envelope of 

uncertainty around the model results (Hansen et al. 2019; Bracis et al. 2020). 

Sensitivity analyses, aiming to quantify the relationships between model inputs (especially 

parameters) and the response, are common diagnostic tools used in model development and 

validation. Sensitivity analyses are used to identify critical and non-critical model parameters. 

Such analyses help focus efforts around estimation and calibration of the important parameters 

and provide an assessment of whether the model sensitivity reflects processes that have an 

impact on the dynamics of the natural ecosystems. 

We conducted sensitivity analysis in four model areas: (1) recycling of nutrients; (2) groups with 

high trophic impact; (3) biomass of meso- and subphotic groups (excluding the bottomfish and 

uku groups); and (4) parameters of pH and temperature effect size. 

Recycling of nutrients

Atlantis has various optional routines that affect the nutrient dynamics. Since our model 

represents an oligotrophic system, the recycling of nutrients is key to the overall productivity of 

the system. Processes that influence nutrient turnover and availability in vertical water layers 

include water column mixing, atmospheric deposition (especially important for waters around 

the volcanically active Hawaiʻi Island), resuspension, and decay (burial of nutrients). To check 

the model sensitivity to these variables, we developed a NPZD (nutrient, phytoplankton, 

zooplankton, and detritus) model and compared model output with and without these options 

included in the model dynamics. We looked at the change in phytoplankton between initial 

biomass and the end biomass after a 5-year simulation using the parameterized NPZD (nutrients, 

phytoplankton, zooplankton and detritus) model. The objective was to keep the end–to–start

biomass ratio < 3 (Figure 15).
43

Figure 15. Ratio of end to start biomass of the large phytoplankton group based on a 5-

year simulation run adding the decay function (decay-wc), including atmospheric 

deposits (include_atmosphere), three depths set as the deep mixing depth 

(mix_deep_depth) and the water column mixing coefficient (wc_kz).

Based on these sensitivity results (Figure 15), we included the decay function and atmospheric 

deposits (Carrillo et al. 2002), set the deep water mixing depth at 400 m and the water column 

mixing coefficient at 1.28*10-9. 

Groups with high trophic impacts

The Ecopath software (Christensen and Walters 2004) can assess the relative trophic impact the

change in biomass of one group has on another group in an ecosystem by simultaneously 

assessing the direct predator-prey interaction and the indirect consequences of both positive and 

negative impacts of trophic interactions (Ulanowicz 1986). Based on these mixed trophic impact 

factors of the developed EwE model for Hawaiʻi (Weijerman et al. 2020), we identified the 

species with the highest impact factors, i.e., the functional group for which a biomass change has 

the most influence on other groups. The following criteria were used: (1) the sum of the absolute 

impact factor across all functional groups was at least 4; and (2) the number of functional groups 

impacted was at least 6. The resulting groups were the following: Sharks, Large Phytoplankton, 

Large Zooplankton, Medium Zooplankton and Detritus (Appendix H). This is very important 

information and in any application of the HI Atlantis model, the user is advised to do multiple 

simulations to account for this sensitivity. For example, the initial biomass and growth rate can 

be changed by ± 25% for one group at a time (8 simulations) and for the combined groups with 

the highest change (2 simulations). The subsequent results will provide an uncertainty envelope 

which can then be taken into account.

Biomass of meso- and subphotic group

Lastly, because biomass estimates of the meso- and subphotic groups (other than the 2 

bottomfish groups and uku) were not available and were estimated based on assumptions (see 

“Step 3. Model initialization” and Appendix E), we conducted a Monte Carlo assessment 
44

whereby the biomass of the 5 meso- and subphotic functional groups was allowed to change with 

±30%. From the mixed trophic impact analysis we know that these groups mostly influence each 

other but also bottomfish that feed on substrate invertebrates (BFB) and octopus (Appendix H). 

Results from 20 Monte Carlo simulations, each trying 15 Ecopath input value combinations, 

show that the absolute biomass of the 5 meso- and subphotic functional groups changed 

minimally ± 0.05 g/m2 for subphotic planktivores and at a maximum of ± 0.23 g/m2 for 

mesophotic benthic carnivores, likely resulting from predator-prey relations, but that through 

trophic relations the biomass of BFB and octopus did not really change with a mean SD of 

0.0006 and 0.0001, respectively (Figure 16). We therefore conclude that even though the 

biomass of the meso- and subphotic groups could be 30% more or less, model outcome will be 

similar. 

Figure 16. Monte Carlo simulations of changing the initial biomass of 5 meso-and 

subphotic functional groups (MBP, MBC, MPL, SBC, SPL) with 30% showed very little 

change in biomass of bottomfish group (BFB). Values show resulting biomass in blue 

line and 1 SD in black bars.

Parameters of pH and temperature effect size

To assess the sensitivity of the added survival scalar values related to changes in ocean 

temperature and ocean warming (Appendix B), we ran simulations where (1) we changed the 

calculation of the scalars, and (2) we increased and decreased both pH and Q10 scalars by 10%. 

In the first case, we ran simulations where we compared our initial calculation of the survival 

scalars, which included all studies related to temperature and where we set the maximum 

survival scalar related to pH to a 30% increase (“unadjusted”) with (a) only including studies

where the temperature change in experiments was less than 5°C (“under 5”) and (b) where also 

the minimum scalar was set to 30% (i.e. maximum decrease is 30%) for the most sensitive group 

and all values with a negative relationship with pH were scaled accordingly (Figure 17A). In the 

second case, we increased and decreased by 10% the adjusted survival scalars (Figure 17B). 

Not too surprisingly, the model was most sensitive to bounding the survival scalar related to pH. 

These groups had survival scalars of 0.1 – 1.3 in the “unadjusted” calculations, whereas in the 
45

adjusted calculations the lowest was 0.7. This change was most noticeable in reef benthic 

piscivores (“RBP”) and sharks (“SHR”) and in the small phytoplankton and benthic bacteria 

groups. Calculating the survival scalar related to temperature based on only studies that included 

a maximum temperature change of 5°C, resulted in lower biomass of small phytoplankton but 

when we also bounded the minimum survival scalar related to pH, the phytoplankton biomass 

was higher than when using the unadjusted scalars. These results correspond with other studies 

that also show an increase in small phytoplankton and decrease in large phytoplankton when 

looking at the combined effects of pH and temperature (Seifert et al. 2020). 

Changing the survival scalars by 10% shows that for groups where we had a relationship from 

the meta-analysis (Appendix B), biomass increased or decreased when this relationship was 

positive or negative, respectively, e.g., the groups RBP and SHR. Trophic ripple effects are 

noticeable for the other groups, especially on planktivorous fishes driven by changes in the 

plankton biomass and on most reef fishes (RGR, RPA, RBC, ROF) (Figure 17B).

In general, the most sensitive groups are corals, and the large and small phytoplankton and 

detritus groups (Figure 17), the later forming the base of the food web and changes in the 

biomass of those groups can have a noticeable ripple effect to higher trophic levels starting with 

zooplankton and ending with dolphins and monk seals that prey on planktivorous fishes. 

Best practice taking uncertainty into account

From the above mentioned results and the trophic impact sensitivity assessments, it is clear that 

the system is mostly driven by bottom-up forces which means that the lower trophic levels have 

an important influence on the ecosystem. Therefore, best available data should be used for the 

parameters associated with their growth and climate-change-related mortality (i.e., the survival 

scalars) and should have an envelope of uncertainty in any future applications. 
46

Figure 17. Sensitivity to survival scalars of organisms' response to (A) adjusting 

calculation of the pH and temperature survival scalars and (B) increasing and decreasing 

these scalars by 10%. Codes of the functional groups on the vertical axis are explained in 

Table 1.
47

Step 6. Model Application: Climate Change Impacts to Fisheries and 

Ecosystem Productivity

To initialize the model in 2020, we used the output of our validated historical model (see Step 4) 

and created a new initial condition file. Direct impacts of ocean warming were included as 

changes in metabolic rate parameters based on a meta-analysis (see “Sensitivity in marine 

organisms” in section 3B) and thermal-induced coral mortalities. Direct impacts of ocean 

acidification included a change in the survival scalar of sensitive organisms (see “Sensitivity of 

marine organisms” in section 3B). Since the groups are sensitive to the values of these scalars, 

we also included a simulation where we increased those values by 10% and one where we 

decreased them by 10% to reflect the uncertainty. The simulated projected change in ocean 

acidity (pH), aragonite saturation and temperature were based on the Coupled Model 

Intercomparison Project 5 (CMIP 5.0) AR8.5 scenario climate models (see “Environmental 

Drivers” in section 3B). 

To assess the influence of climate change on the ecosystem and fisheries, we compared the mean 

of the last 5 years of a 50-year simulation (2020–2070) with the effects of climate change to a 

base case where we did not include climate change impacts. We specifically looked at the 

following social-ecological16 indicators for a sustainably fishery and a productive ecosystem 

(Step 1): 

• S: Viewing option of charismatic megafauna (calculated as biomass of monk seals, 

dolphins and sea turtles)

• S: Biomass of cultural important species catches (groups included are parrotfishes 

[RPA], reef benthic carnivores [RBC], bottomfish [BFB, BFW], lobster [LOB], and 

octopus [OCT])

• S: Revenue from catches

• S: Trophic level of the catch

• E: Trophic level of the marine community (primary producers were excluded)

• E: Ratio demersal to planktivores fish groups

• E: Biomass of apex predators (sharks [SHR], jacks [PIS], reef [RBP] and deep-water 

[MBP] piscivores)

• E: Ratio of calcifiers (corals and CCA) to fleshy algae (MAF, TRF)

Values were all standardized to the mean. Results show that while values for socio-economic 

indicators were comparable between the base case and the climate scenarios, the ecosystem 

structure and functioning was dramatically impaired by climate change (Figure 18). 

 

16 Social = S

 Ecological = E
48

Figure 18. Standardized social-ecological effects of climate change compared to a base 

case scenario. The top panel shows the mean of the socioeconomic and ecological 

indicators. In the bottom panel, the left-most four bars represent socio-economic 

indicators and the right four bars ecological indicators. The error bars are based on 

parameters reflecting the sensitivity of organisms’ survival scalar to changes in pH and 

temperature and show results of 10% increase and 10% decrease in these parameter 

estimates.
49

Step 7. Conclusions and Next Steps

For the development of any model, the first step is a scoping activity to identify the objectives of 

the stakeholders to select a model framework that can address those needs (Step 1). Objectives 

identified for the inhabited islands of Hawaiʻi included economic (e.g., Maintain marine 

revenue), social (e.g., Maintain food provisioning), cultural (e.g., Maintain intangible benefits) 

and biological objectives (e.g., Maintain resilient ecosystems). For each of the eight identified 

objectives, indicators were also selected to measure performance toward reaching these goals 

under alternative management and climate scenarios (Step 2). 

We developed an Atlantis model for the nearshore ecosystems from 0 to 400 m depth (Step 3).

Data synthesis was a major part of this model development since thousands of parameters needed 

to be parameterized. Data came from PIFSC programs, University of Hawaiʻi, and literature 

reviews. Largest uncertainties around parameter values were diet composition of especially noncommercial deep-water species, spatial distribution of the mesopelagic scatter layer (i.e., 

myctophids), spatial distribution and habitat affinity of juvenile fish of most groups, spatial 

distribution of plankton community, and sensitivity of fishes and marine mammals to 

temperature increase and pH decrease (Table 6).

Table 6. Pedigree of model parameters. Values are confidence intervals of the values 

used in the model and reported under Step 3. NA = not available.

Category Biomass

Spatial 

distribution Diet

Life history 

characteristics

Sensitivity to 

OA and OW

Marine 

mammals

0.3 dolphins

0.1 monk seals

0.3 dolphins

0.1 monk seals

0.3 dolphins

0.1 monk seals

0.3 dolphins

0.1 monk seals

NA

Sea turtles 0.3 0.3 0.3 0.2 NA

Reef fish 0.2 0.2 0.2 0.3 NA

Meso and sub 

benthic 

piscivores

0.5 0.3 0.4 0.6 NA

Mesophotic 

fish

0.5 0.3 0.4 0.6 NA

Subphotic fish 0.5 0.3 0.4 0.6 NA

Bottomfish 0.2 0.2 0.2 0.1 NA

Invertivorous 

rays

0.5 0.3 0.4 0.6 NA

Coastal 

pelagics

0.3 0.3 0.3 0.4 NA

Mesopelagic 

scatter layer

0.5 NA 0.4 0.6 NA

Mobile inverts 0.5 0.5 0.4 0.5 0.4

Meiobenthos 0.5 0.5 0.4 0.5 0.4

Filter feeders 0.5 0.3 0.5 0.6 0.4
50

Category Biomass

Spatial 

distribution Diet

Life history 

characteristics

Sensitivity to 

OA and OW

Structural 

benthic species

0.2 0.2 0.2 0.2 0.2

Primary 

producers

0.3 0.5 × 0.4 0.2

Micronekton 0.3 0.5 0.4 0.4 0.2

A hindcast simulation showed strong model skill when compared with observational data (Step 

4). Additionally, the modeled ecosystem conforms general ecological rules, such as, a trophic 

pyramid with few apex predators and a large base of primary producers and detritus eaters, and a 

linear relationship between log-biomass and size/trophic level. As models increase in realism 

they also increase in complexity, making it harder to estimate uncertainties. Therefore, we

conducted sensitivity analyses on model components we deemed most important or we had no 

good data for (Step 5). Based on these results we set parameter values that influence nutrient 

recycling at the most plausible values (i.e., where plankton did not increase more than 3 times 

the initial biomass). We concluded that the initial biomass of 5 meso- and subphotic fish groups, 

has a very small influence on commercially important bottomfish, therefore not knowing the 

exact biomass is of minor importance. However, uncertainty with regards to the biomass and/or 

growth rate of sharks and lower trophic levels should be taken into account as a small change in 

the biomass of these group will have a large impact on other groups. Advice is to bound these 

parameters between a plausible maximum and minimum rate to show the envelope of uncertainty 

around the outcomes in future applications. The model is also sensitive to especially the survival 

scalars of changes in pH, this largely affected phytoplankton with ripple effects to higher trophic 

levels and to a few fish species. Again, bounding these parameter values between a maximum 

and minimum will include the uncertainty around the results. 

Finally we applied the model to project future climate change impacts to the ecosystem and the 

goods and services it supplies us focusing on four socio-economic indicators and four ecological 

indicators (Step 6). Our results indicated that climate change will mostly affect the coral reef 

ecosystem structure with an increase in planktivores and a decrease in corals. Long-term effects 

could include a flattening of the reef (Alvarez-Filip et al. 2009) which will likely result in a less 

productive ecosystem since the habitat for many species will be severely degraded (Darling et al. 

2017; González-Rivero et al. 2017). 
51

Acknowledgements

Atlantis models are extremely data hungry, hence the development of this complex model was 

dependent on many people from various organizations and most disciplines in providing data or 

expert opinion. The list is too long to include everybody by name but in particular I would like to 

thank Beth Fulton, Bec Gorton and Javier Porovic from CSIRO for their expert Atlantis software 

expertise and help throughout the project. I would further like to thank my colleagues at PIFSC 

who have provided data and/or expert opinions and fruitful discussions. I am particularly grateful 

for the help from Ivor Williams, Rhonda Suka, Tomoko Acoba, Johanna Wren, Hannah Barkley, 

Jennifer Raynor, Tom Oliver, Kaitlyn Lowder, Zack Oyafuso, Kirsten Leong, Don Kobayashi, 

Reka Domokos, Joey Lecky, Brian Langseth, Ben Richards, Brett Taylor, Bruce Mundy, Joseph 

O’Malley, Ray Boland, Allen Andrews, Dayton Dove, Erin Oleson, T. Todd Jones, Stacie 

Robinson, Jake Asher, Molly Timmers, Pearl Trick, Summer Martin, Jamie Barlow, Frank 

Parrish, Justin Hospital, Ryan Nichols, Alohi Nakachi, Lansing Perng, and Elena Cosner. 

Additionally, I would like to acknowledge my colleagues at other science centers for their 

support and help, in particular Isaac Kaplan, Raphael Girardin, Ryan Morse and Jason Link. Last 

but not least, I would like to thank the support in either data provisioning or expert opinion from 

staff at the University of Hawaiʻi at Mānoa, in particular Virginia Moriniake and Chris Kelly at 

HURL; Jeff Drazen, Brian Powell and Jim Potemra at the Oceanography Department, Kirsten 

Oleson at NREM; Craig Nelson & Tara Clemente at C-More; and Chelsie Counsell, Eileen 

Nalley, and Megan Donahue from HIMB.

This project was partially funded by the NOAA Ocean Acidification Program. 
52

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Appendix A: Characteristics of spatial areas in Atlantis

Table A1 shows the number of the spatial area (BoxID), the (modeled) area of that box in m2, the 

number of vertical water layers in that box, the maximum depth for the box and the percentage 

hard and soft substrate.

Table A1. Characteristics of the spatial areas in HI Atlantis 

BoxID

Modeled 

Area (m2)

# Watercolumn 

Layers

Max Depth 

(m)

Hardbottom 

(%)

Softbottom 

(%)

0 1000000000 3 -2000 NA NA

1 86837950 3 -400 0.72 0.28

2 70314411 2 -150 0.62 0.38

3 20550660 1 -30 0.96 0.04

4 45565382 1 -30 0.86 0.14

5 98085777 2 -150 0.66 0.34

6 254311049 3 -400 0.64 0.36

7 1000000000 3 -2000 NA NA

8 571491897 3 -400 0.23 0.77

9 134529397 2 -150 0.14 0.86

10 41544665 1 -30 0.52 0.48

11 44769089 1 -30 0.73 0.27

12 304230803 2 -150 0.74 0.26

13 344581628 3 -400 0.72 0.28

14 10000000000 3 -2000 NA NA0

15 10000000000 3 -2000 NA NA

16 54796911 3 -400 0.92 0.08

17 96074157 2 -150 0.66 0.34

18 28884198 1 -30 0.85 0.15

19 51587979 3 -400 0.60 0.40

20 100678121 2 -150 0.38 0.62

21 25334113 1 -30 0.84 0.16

22 9192920 1 -30 0.84 0.16

23 27258554 2 -150 0.66 0.34

24 28187831 1 -30 0.78 0.22

25 290936862 2 -150 0.37 0.63

26 40355117 1 -30 0.75 0.25

27 738431773 3 -400 0.31 0.69

28 474137398 2 -150 0.53 0.47

29 36519920 1 -30 0.57 0.43

30 55711796 1 -30 0.40 0.60

31 109103710 2 -150 0.33 0.67

32 170414432 3 -400 0.54 0.46
69

BoxID

Modeled 

Area (m2)

# Watercolumn 

Layers

Max Depth 

(m)

Hardbottom 

(%)

Softbottom 

(%)

33 81283687 2 -150 0.11 0.89

34 13387472 1 -30 0.78 0.22

35 9324691 1 -30 0.88 0.12

36 76510477 2 -150 0.18 0.82

37 859092447 3 -400 0.46 0.54

38 111378576 2 -150 0.30 0.70

39 46808198 1 -30 0.68 0.32

40 8964789 1 -30 0.53 0.47

41 292676476 2 -150 0.51 0.49

42 51281407 2 -150 0.37 0.63

43 28296839 1 -30 0.72 0.28

44 43260763 1 -30 0.71 0.29

45 190212033 2 -150 0.45 0.55

46 155688989 3 -400 0.23 0.77

47 325411852 3 -400 0.46 0.54

48 1007017807 2 -150 0.56 0.44

49 46468590 1 -30 0.78 0.22

50 69618586 1 -30 0.72 0.28

51 10000000000 3 -2000 NA NA

52 70598569 3 -400 0.79 0.21

53 98572918 2 -150 0.72 0.28

54 46144471 1 -30 0.88 0.12

55 115592299 1 -30 0.70 0.30

56 81960784 2 -150 0.65 0.35

57 84092178 3 -400 0.61 0.39

58 75651692 1 -30 0.88 0.12

59 195332834 2 -150 0.73 0.27

60 250847920 3 -400 0.78 0.22

61 138630762 3 -400 0.81 0.19

62 102303859 2 -150 0.75 0.25

63 45720462 1 -30 0.74 0.26

64 103824036 1 -30 0.76 0.24

65 33338683 3 -400 0.82 0.18

66 58885984 2 -150 0.33 0.67

67 65975190 1 -30 0.74 0.26

68 72306447 3 -400 0.51 0.49

69 128849063 2 -150 0.69 0.31

70 89814831 1 -30 0.76 0.24

71 86298312 1 -30 0.78 0.22

72 110958495 2 -150 0.79 0.21
70

BoxID

Modeled 

Area (m2)

# Watercolumn 

Layers

Max Depth 

(m)

Hardbottom 

(%)

Softbottom 

(%)

73 69227275 3 -400 0.56 0.44

74 67686243 3 -400 0.69 0.31

75 79391217 2 -150 0.48 0.52

76 14365727 1 -30 0.73 0.27

77 17858498 1 -30 0.91 0.09

78 107580948 2 -150 0.60 0.40

79 64760693 3 -400 0.65 0.35

80 10000000000 3 -2000 NA NA

81 island 0 0 0 0

82 island 0 0 0 0

83 island 0 0 0 0

84 island 0 0 0 0

85 island 0 0 0 0

86 island 0 0 0 0

87 island 0 0 0 0

88 island 0 0 0 0
71

Appendix B: Meta-analysis of sensitivities to pH and temperature 

of Hawaiian organisms

Contributor: Kaitlyn Lowder

From an initial screening of 1,543 full papers, we gathered and analyzed data from 334 papers of 

controlled experiments that exposed organisms from tropical Pacific or Indo-Pacific 

environments to either changes in pH or temperature. The database contains 2,561 responses of 

243 unique species/genera belonging to 29 functional groups. Corals (PRM, PRM, MON, and 

POC) were most represented in the studies (Figure A1). Also, various life stages of small 

planktivorous fish species (aggregated in the functional group reef small planktivores, RSP; e.g., 

Amphiprion ocellaris, Pomacetrus wardi) were highly represented in the studies. We had no data 

on most of the fish species, deep sea corals in the genus Leptoseris, or manta rays. 

As the Atlantis model can assimilate variables for growth, nutritional quality, reproduction, and 

predation vulnerability, responses of interest encompassed basic responses such as survival to 

behavioral responses like avoidance of predator cue. Yet, the majority of the studies explored the 

response in biomass growth, largely estimated through change in the rate of growth, 

calcification, consumption, mortality, and survival. Response values and sample sizes from 

individual studies were converted to effect size (log odds ratio or Hedges’ g, depending on 

whether the data were continuous or binary), then related to changes in temperature or [H+]. 

Likelihood ratio tests were used to compare and select intercept-only, linear, and quadratic 

models of best fit for each functional group and response.

In Atlantis, there can only be one relationship that describes the response of the four variables 

per functional group. To determine this relationship, we aggregated effect sizes for studies with 

multiple response measurements using Borenstein et al. (2009) procedures so meta-regression 

models were fit with independent data. Depending on the available data, in some cases we found 

both a continuous and binary model fit for the same group. In these cases, we chose the model fit 

with the lowest log likelihood. If the log likelihood was <10% different we chose the linear or 

quadratic fit over the intercept-only fit. 

Figure A1. Number of experiments per Atlantis functional group for relationships of 

changes in pH (blue bar) and temperature (red bar).
72

pH relationships

For relationships with pH where the best fit was linear or quadratic, we developed a single pH 

sensitivity curve of pH against the functional group pH sensitivity factor (Busch and McElhany 

2016). With a change in pH the survival of the group changes according to the corresponding 

value of this curve. To standardize these curves, we used the functional group that showed the 

most negatively responding relationship as a reference relationship. We scaled this relationship 

to a range of 1 at current pH (8.1) to 0 at pH=7.0 by setting the intercept at 1 for pH=8.1 and 

adjusting the slope so that the survival equals zero at pH=7.0. We then scaled the rest of the 

relationships to that reference curve by multiplying the slopes with the same value and setting all 

the intercepts to 1. This generated values of all the other functional groups proportionally less to 

pH. 

For relationships where the best fit was an intercept model, we applied it as a piecewise function 

where at or above current pH, the sensitivity scalar is set to 1 and with a pH below 8.0 the scalar 

either step-increases or step-decreases and stays constant at further decreased pH values. All 

intercept values were standardized to a range of 0.1 and 1.3 assuming that under lower pH 

conditions the groups with a positive relationship with pH increased their survival with a 

maximum of 30% and those with a negative relationship decreased their survival to a minimum 

of 10%.

We looked for a natural break in pH and found one at around a pH value of 7. Therefore, we did 

not include any further decreases in pH (these results were often outliers addicting the curve of 

the shape disproportionately). Results showed that statistically the intercept model was in many 

cases the best fit, i.e., the data have no relationship to pH, and the effect size was for 8 groups 

positive and for 7 negative (Figure A2A). The pelagic bacteria (PB) and medium zooplankton 

(ZM) groups had the most positive and negative response and were set at 30% increase and 30% 

decrease with respect to the survival scalar, respectively. All other groups were scaled 

accordingly. For 8 functional groups the best fit was a linear relationship with pH and all groups 

showed a negative response with a decrease in pH (Figure A2B). The functional group with the 

most negative sensitivity was bivalves (BIV). This group served as the reference curve and all 

others were scaled accordingly.

Figure A2. Relationship between a decrease in pH and survival of functional groups for 

(A) intercept only models and (B) linear models.
73

In correspondence with other studies (e.g., Kroeker et al., 2013), most calcareous groups (such as 

bivalves, corals, urchins, shrimps, calcareous algae, coccolithophores) decreased in biomass 

growth and plankton increased. However, contrary to Kroeker et al.’s finding, tropical crustose 

coralline algae actually increased its biomass growth under more acidic conditions. 

Temperature relationships

For temperature sensitivity to growth related parameters, we calculated a single ‘effect size’ so 

with a change in temperature a functional group either increases its metabolic rate/growth when 

the effect size was >0 or decreases. Temperature can have a large influence on many 

biogeochemical and ecological processes. We only included results of studies where the 

temperature change was less than 5°C as higher increases are likely causing physiological 

responses. In Atlantis temperature effects are mostly applied through the “Tscalar” which is 

based on a set reference temperature of 15°C:

TT TTTTTTTTTT = qq10

TTTTTTTTTTTTTT − TTTTTTff

10

where q10 is a species-specific parameter the user needs to provide and is typically set to 2

which means that with an 10°C increase in ocean temperature, rate parameters will double 

(Tscalar=2). 

Effect size ranged between -3.76 (n=19, AIC=185) for small phytoplankton, indicating that with 

an increase in temperature, growth decreases, and 3.24 (n=12, AIC=57) for benthic detritivores 

(Figure A3). We standardized these values so that an effect size of 2 resulted in a q10 of 2 and 

set the minimum to 0.1 (i.e., with a 10°C increase in ocean temperature, rate parameters will 

drop to 10%). This resulted in standardized values of q10 between 0.1, for sharks and small 

phytoplankton, and 3.52 for pelagic bacteria. 
74

Figure A3. Effect size of the relationship between an increase in temperature and survival 

of functional groups. 
75

Appendix C: Vertebrate species aggregated by functional groups

All species were aggregated in functional groups based on diet, habitat, life history parameters 

and social or economic importance. Table A2 shows the composition of each group with the 

code used throughout this report. For the reef fishes we only included the species that had a 

combined biomass and/or abundance proportion of approximately 85%.

Table A2. Species composition of the functional groups used in HI Atlantis. Purple lines 

are broad categories and other colors are the functional groups.

Code Group Family Scientific Name

Reef Fishes

RSP Small Planktivores

Pomacentridae Chromis vanderbilti

Pomacentridae Chromis verater

Pomacentridae Dascyllus albisella

Chaetodontidae Heniochus diphreutes

Pomacentridae Abudefduf abdominalis

Acanthuridae Acanthurus thompsoni

Chaetodontidae Chaetodon kleinii

Pomacentridae Chromis hanui

Pomacentridae Abudefduf vaigiensis

RLP Large Planktivores

Balistidae Melichthys niger

Acanthuridae Naso hexacanthus

Acanthuridae Naso brevirostris

Holocentridae Myripristis berndti

Holocentridae Myripristis kuntee

Holocentridae Myripristis amaena

RCO Corallivores

Chaetodontidae Chaetodon ornatissimus

Chaetodontidae Chaetodon multicinctus

Chaetodontidae Chaetodon quadrimaculatus

Pomacentridae

Plectroglyphidodon 

johnstonianus

Chaetodontidae Chaetodon unimaculatus

Chaetodontidae Chaetodon lunulatus

ROF Invertivores

Balistidae Sufflamen fraenatum

Balistidae Sufflamen bursa

Labridae Thalassoma duperrey
76

Code Group Family Scientific Name

Balistidae Rhinecanthus rectangulus

Cirrhitidae Paracirrhites arcatus

Zanclidae Zanclus cornutus

Labridae Coris gaimard

Chaetodontidae Chaetodon lunula

Labridae Stethojulis balteata

Labridae Thalassoma ballieui

Tetraodontidae Canthigaster jactator

Labridae Coris venusta

Gomphosus varius

Halichoeres ornatissimus

Monacanthidae Cantherhines dumerilii

Forcipiger flavissimus

Tetraodontidae Arothron meleagris

RBC Target Benthic Carnivores

Lutjanidae Lutjanus kasmira

Labridae Bodianus bilunulatus

Mullidae Parupeneus multifasciatus

Lutjanidae Lutjanus fulvus

Mullidae Parupeneus cyclostomus

Mullidae Mulloidichthys flavolineatus

Mullidae Mulloidichthys vanicolensis

Mullidae Parupeneus insularis

Labridae Coris flavovittata

Mullidae Parupeneus pleurostigma

Sargocentron diadema

RBR Target Browsers

Acanthuridae Naso lituratus

Kyphosidae Kyphosus sp

Acanthuridae Naso unicornis

Scaridae Calotomus carolinus

Chanidae Chanos chanos

RGR Target Grazers

Acanthuridae Acanthurus olivaceus

Acanthuridae Acanthurus nigrofuscus

Acanthuridae Acanthurus dussumieri

Acanthuridae Acanthurus blochii

Acanthuridae Ctenochaetus strigosus

Balistidae Melichthys vidua
77

Code Group Family Scientific Name

Acanthuridae Zebrasoma flavescens

Acanthuridae Acanthurus triostegus

Acanthuridae Acanthurus xanthopterus

Acanthuridae Acanthurus leucopareius

Acanthuridae Acanthurus nigroris

Pomacentridae Stegastes fasciolatus

RPA Parrotfishes

Scaridae Scarus rubroviolaceus

Scaridae Chlorurus spilurus

Scaridae Scarus psittacus

Scaridae Scarus dubius

Scaridae Chlorurus perspicillatus

Scaridae Scaridae

RBP Benthic Piscivores

Serranidae Cephalopholis argus

Muraenidae Gymnothorax flavimarginatus

Labridae Oxycheilinus unifasciatus

Muraenidae Gymnothorax meleagris

Cirrhitidae Paracirrhites forsteri

Aulostomidae Aulostomus chinensis

Muraenidae Gymnothorax javanicus

PIS Roving Piscivores

Carangidae Caranx melampygus

Carangidae Caranx ignobilis

Carangidae Seriola dumerili

Carangidae Seriola rivoliana

Lutjanidae Aphareus furca

Carangidae Carangoides ferdau

Carangidae Fistularia commersonii

Carangidae Carangoides orthogrammus

Carangidae Caranx ferdau

Fistulariidae Scomberoides lysan

Carangidae Elagatis bipinnulata

Belonidae Tylosurus crocodilus

Sphyraenidae Sphyraena barracuda

Gempylidae Rexea nakamurai

Berycidae Beryx decadactylus

Fistulariidae Fistularia petimba
78

Code Group Family Scientific Name

SHP Shallow Prey

Carangidae Decapterus macarellus

Carangidae Selar crumenophthalmus

Carangidae Decapterus sp.

Sharks and Rays

SHR Sharks

Squalidae Squalus mitsukurii

Carcharhinidae Carcharhinus plumbeus

Carcharhinidae Carcharhinus galapagensis

Carcharhinidae Carcharhinus amblyrhynchos

Carcharhinidae Triaenodon obesus

Carcharhinidae Carcharhinus altimus

Carcharhinidae Galeocerdo cuvier

Squalidae Squalus blainvillei

Cetorhinidae Selachii

Carcharhinidae Galeocerdo cuvier

IVR Invertivorous Rays

Myliobatidae Aetobatus narinari

Dasyatidae Bathytoshia lata

Plesiobatidae Plesiobatis daviesi

Dasyatidae

Batoidea 

RAY

Manta 

Rays Mobulidae Mobula birostris

Deep-water Fishes

BFW Bottomfish—Water Column

Lutjanidae Pristipomoides filamentosus

Lutjanidae Etelis coruscans

Lutjanidae Pristipomoides sieboldii

Lutjanidae Aphareus rutilans

BFB Bottomfish—Bottom

Lutjanidae Etelis carbunculus

Epinephelidae Hyporthodus quernus

Lutjanidae Pristipomoides zonatus

UKU Uku Lutjanidae Aprion virescens
79

Code Group Family Scientific Name

Mesophotic Fishes

MPL Mesophotic Planktivores

Pomacentridae Chromis verater

Chaetodontidae Chaetodon miliaris

Chaetodontidae Heniochus diphreutes

Pomacentridae Chromis leucura

Acanthuridae Naso hexacanthus

Serranidae Pseudanthias Hawaiiensis

Pomacentridae Dascyllus albisella

Acanthuridae Naso spp.

Serranidae Luzonichthys earlei

Pomacentridae Chromis spp.

Acanthuridae Naso maculatus

Balistidae Melichthys niger

Pomacentridae Chromis ovalis

Holocentridae Myripristis berndti

Ostraciidae Lactoria diaphana

MBC Mesophotic Benthic Carnivores

Lutjanidae Lutjanus kasmira

Balistidae Sufflamen fraenatum

Mullidae Parupeneus multifasciatus

Zanclidae Zanclus cornutus

Pomacanthidae Apolemichthys arcuatus

Labridae Bodianus albotaeniatus

Holocentridae Myripristis chryseres

Acanthuridae Acanthurus spp.

Bothidae Bothus thompsoni

Tetraodontidae Sphoeroides cutaneus

Chaetodontidae Forcipiger flavissimus

Mullidae Parupeneus pleurostigma

Tetraodontidae Torquigener florealis

Chaetodontidae Forcipiger spp.

Mullidae Parupeneus chrysonemus

Labridae Pseudojuloides cerasinus

Apogonidae Ostorhinchus maculiferus

Acanthuridae Acanthurus spp.

Chaetodontidae Chaetodon kleinii

MBP Meso and Subphotic Benthic Piscivores

Scorpaenidae

Pontinus macrocephalus Pontinus macrocephalus
80

Code Group Family Scientific Name

Chlorophthalmus 

proridens Chlorophthalmus proridens

Gymnothorax berndti Gymnothorax berndti

Polymixia berndti Polymixia berndti

Ariosoma marginatum Ariosoma marginatum

Gymnothorax spp. Gymnothorax spp.

Conger oligoporus Conger oligoporus

Synodus kaianus Synodus kaianus

Hoplichthys citrinus Hoplichthys citrinus

Trachinocephalus myops Trachinocephalus myops

Gymnothorax nuttingi Gymnothorax nuttingi

Chrionema spp. Chrionema spp.

Subphotic Fishes

SBC Subphotic Benthic Carnivores

Ophichthidae

Monacanthidae Pervagor spilosoma

Synaphobranchidae Meadia abyssalis

Serranidae Zalanthias kelloggi

Percophidae Chrionema chryseres

Bothidae Poecilopsetta Hawaiiensis

Scorpaenidae Setarches guentheri

Chaunacidae Chaunax umbrinus

Bothidae Parabothus chlorospilus

Bothidae Taeniopsetta radula

Peristediidae Scalicus engyceros

Ogcocephalidae Halieutaea retifera

Pinguipedidae Parapercis roseoviridis

Chaetodontidae Chaetodon tinkeri

Tetraodontidae Canthigaster coronata

Congridae Gnathophis spp.

Moridae Laemonema rhodochir

Percophidae Chrionema squamiceps

Acropomatidae Parascombrops argyreus

Lophiidae Lophiodes miacanthus

Bothidae Arnoglossus debilis

Dactylopteridae Dactyloptena orientalis

Moridae

Lutjanidae Randallichthys filamentosus

Bothidae Parabothus coarctatus

Moridae Physiculus spp.

Cepolidae Owstonia Hawaiiensis
81

Code Group Family Scientific Name

Aulopodidae Hime japonica

Bothidae Chascanopsetta prorigera

Bothidae Engyprosopon xenandrus

Ophichthidae Myrophinae

Chaetodontidae Forcipiger longirostris

SPL Subphotic Planktivores

Symphysanodontidae Symphysanodon maunaloae

Caproidae Antigonia spp.

Serranidae Odontanthias elizabethae

Chaetodontidae Roa excelsa

Symphysanodontidae Symphysanodon typus

Triacanthodidae Hollardia goslinei

Callanthiidae Grammatonotus spp.

Serranidae Odontanthias fuscipinnis

Caproidae Antigonia eos

Priacanthidae Priacanthus alalaua

Pomacentridae Chromis struhsakeri

Epigonidae Epigonus spp.

Callanthiidae Grammatonotus laysanus

Pinguipedidae Parapercis schauinslandii

Priacanthidae Cookeolus japonicus

Caproidae Antigonia steindachneri

Emmelichthyidae Erythrocles scintillans

Balistidae Xanthichthys auromarginatus

Serranidae Pseudanthias thompsoni

Priacanthidae Priacanthus meeki

Serranidae Pseudanthias fucinus

Argentinidae Glossanodon struhsakeri

Epigonidae Epigonus glossodontus

Mesopelagic Scatter Layer

MSL

Myctophidae Benthosema fibulatum

Myctophidae Idiolychnus urolampus

Myctophidae

Myctophidae Lampadena urophaos

Myctophidae Diaphus adenomus

Myctophidae Diaphus termophilus

Bregmacerotidae Bregmaceros sp.

Myctophidae Diaphus spp.
82

Code Group Family Scientific Name

Marine Reptiles

GT Cheloniodae Chelonia mydas

HT Cheloniodae Eretmochelys imbricata

Marine Mammals

MS Phocidae Neomonachus schauinslandi

SPD Delphinidae

Stenella longirostris 

longirostris

BND Delphinidae Tursiops truncatus truncatus
83

Appendix D: Invertebrate data analysis 

Contributor: Molly Timmers

Data collected from Autonomous Reef Monitoring Structures (ARMS)17 by PIFSC’s Reef 

Assessment and Monitoring Program were used for the shallow water (0–30 m) reef invertebrate 

groups. ARMS are long-term collecting devices that attract colonizing invertebrates. Designed to 

mimic, to some degree, the structural complexity of a coral reef, they are composed of 4 open 

and 4 semi-closed layers of 23×23 cm PVC plates. Up to 3 replicates were deployed at each site 

of similar depth and habitat type (fore reef 12–16 m) and remained on the benthos for 3 years. 

Mobile organisms 2 mm and greater from 37 ARMS units at 14 sites retrieved in 2013 and 2016 

were examined. Given the device construction and the motile organismal focus, burrowing 

species, suspension feeders, and sessile organisms will be underrepresented in these data. All 

species were identified to species if possible or the next taxonomic level. Based on available 

literature, organisms were classified into the following trophic guilds: omnivores, carnivores, 

herbivores, suspension feeders, and detritivores. 

Average density was 284 individuals per ARMS unit. Of the >400 identified species, the 

majority (70%) belonged to the phylum Arthropoda, 18% to Mollusca and 4.5% to Annelida 

(Figure A4). Annelids, which can make up 12% of the infaunal biomass and are extremely 

abundant with values up to 43,500 ind/m2 (Kohn and White 1977), were underrepresented in part 

because an ARMS unit is likely not a suitable substrate for the burrowing marine worms. When 

partitioning these data by trophic guild, 40% were omnivorous (mostly crustaceans), 27% 

primarily detritus feeders, 15% carnivorous, 10% herbivorous and 8% suspension feeders 

(Figure A4).This distribution was similar as observed in the East Pacific (Enochs and Manzello 

2012) and Guam (Weijerman et al. 2015). 

Figure A4. Composition of relative species count per (left panel) phylum and (right panel) 

trophic guild from 37 Autonomous Reef Monitoring Structure (ARMS) units.

Biomass estimates of crypto- and epifauna on reefs are difficult to obtain. Ainsworth et al. 

(Ainsworth et al. 2007) reported an infaunal biomass 27 g/m2 and a total epifaunal biomass of 

 

17 https://www.pifsc.noaa.gov/cred/survey_methods/arms/
84

400 g/m2 for a reef system in Indonesia. Bustamante (Bustamante 2010) reported an infaunal 

invertebrate biomass of 6 g/m2

 and a total epifauna excluding urchins, sea cucumbers, and sea 

stars of 9 g/m2 for a subtropical bay in Australia. Enochs (Enochs 2012) estimated the total 

cryptofauna on dead coral for rubble, and high, medium and low degraded reefs as 3.5 

AFDW/m2, 6.2 AFDW/m2, 11.0 AFDW/m2 and 12.3 AFDW/m2, respectively. I used the

complexity estimates (PIFSC RAMP) as proxies for the degradation of a reef with a complexity 

of 2 corresponding to high degraded reef, 3 to medium degraded reef and a complexity of 4 and 

5 to a low degraded reef. Using the trophic composition (Figure A4) and assuming omnivores to 

be the meiofauna groups, I could then estimate the biomass of each functional group. To account

for the macroinvertebrates (e.g., urchins) in the benthic grazer groups, and the benthic detritivore 

group (e.g., sea cucumbers) and the underrepresented suspension feeders from ARMS data, I 

multiplied their biomass by 1.5. Further, I assumed a biomass of 30 g/m2 for infauna (e.g., 

polychaetes). 

I used the trophic composition of the ARMS data (Figure A4) for diet data of meiofauna, equally 

dividing the omnivores over the detritivores, carnivores, herbivores, and suspension feeders. 

Lobster and octopus biomass estimates were based on the assumption of a sustainable fishery 

(see main text). I then compared these values with values based on an Ecopath with Ecosim 

model developed for the same study site (Weijerman et al. In press).

Mean vital rate parameters were calculated based on the production:biomass ratios (P/B for 

growth rate) and consumption:biomass (Q/B for consumption rate) which came from literature or 

empirical relationships with values derived from literature (Table A3). The P/B depends highly 

on the mean annual temperature, mean individual weight, and swimming capacity of marine 

macroinvertebrates (Cartes et al. 2002); therefore, if I did not find any reported values in the 

literature I used an empirical relationship established by Brey (Brey 2001) that includes the 

following aspects:

Log (P/B) = 7.947 – 2.294 * log(M) – 2409.856 * 1/(T+273) + 0.168 * 1/D + 0.194 + 0.180 * 

Infauna/Epifauna + factor * taxon – 0.062 + 582.851 * log(M) * 1/(T+273)

where M is the mean individual body mass in kilojoules, D is the water depth in meter, infauna 

equals 1, epifauna equals 0. Motility and taxon are also taken into account with taxon being 

Annelida, Crustacea, Echinodermata or Insecta .For the Q/B ratio I used the empirical 

relationship established by Cammen (Cammen 1979): 

C = 0.381*W^0.742

where C is the daily consumption in mg dry weight (DW), W is the mean body weight in mg 

DW.

Based on the species or family composition of the benthic small invertebrates derived from 

ARMS, we obtained a weighting factor for the calculation of P/B and Q/B per functional group 

of invertebrates (Table A3).
85

Table A3. The vital rate values, Production over Biomass (P/B) and Consumption over 

Biomass (Q/B) for species groups with their source.

Functional group P/B Source Q/B Source

Benthic Infauna: Fire worms, 

other polychaetes, flat 

worms, sipunculids (peanut 

worms), and nemerteans 

(ribbon worms)

3.9 (Brey 2001; 

McCarthy et al. 

2003; Safarik et al. 

2006; Gribble 

2007)

10.0 (McCarthy et al. 2003; 

Safarik et al. 2006; 

Gribble 2007)

Benthic meiofauna: 

Xanthidae (pebble crabs), 

Pilumnidae (hairy crabs), 

Caridea (snapping shrimp, 

cleaner shrimp), and 

Stomatopoda (mantis shrimp)

2.64 (Werding 1990; 

Ainsworth et al. 

2007; 

Harikrishnan et al. 

2010)

10.52 (Cammen 1979; 

Werding 1990; Opitz 

1996; Ainsworth et al. 

2007; Harikrishnan et al. 

2010)

Lobster and Large crabs 0.827 (Pauly 1980; 

Ainsworth et al. 

2007; O’Malley 

and MacDonald 

2009)

14.88 (Ainsworth et al. 2007)

Octopus 3.313 (Brey 2001; 

Tsehaye and 

Nagelkerke 2008)

8.0 (Van Heukelem 1976; 

Tsehaye and Nagelkerke 

2008)

Benthic carnivores: triton, 

sea slugs, nudibranchs

3.344 (Edwards and 

Huebner 1977; 

Morton 1990; 

Brey 2001; 

Ainsworth et al. 

2007)

11.34 (Edwards and Huebner 

1977; Cammen 1979; 

Morton 1990; Opitz 

1996)

Benthic Detritivores: 

Echinoderms (boring sea 

urchins, sea stars, sea 

cucumbers, crabs)

2.11 (Brey 2001; 

Ainsworth et al. 

2007; Wabnitz et 

al. 2010; 

Weijerman et al. 

2013)

16.13 (Cammen 1979; 

Ainsworth et al. 2007; 

Wabnitz et al. 2010; 

Weijerman et al. 2013) 

Suspension feeders: 

Bivalves, brittle stars

3.722 (Broom 1982; 

Mladenov and 

Emson 1984; 

Ainsworth et al. 

2007)

11.12 (Opitz 1996; Ainsworth 

et al. 2007)
86

Functional group P/B Source Q/B Source

Shallow filter feeders: 

sponges, soft corals

1.067 (Weijerman et al. 

2013)

6.480 (Weijerman et al. 2013)

Deep filter feeders: soft 

corals, black corals, 

anemones

1.2 4.02

Benthic Grazers: free-living 

urchins, sea hares, cowries, 

limpets, top snails, chitons

0.67 (Brey 2001) 8.56 (Cammen 1979; 

Wabnitz et al. 2010)

The annual Q/B values were then converted to daily consumption rates. Since Atlantis uses 

maximum consumption rates, these values were multiplied by 1.2.
87

Appendix E: Meso- and subphotic initial conditions

Benthic cover

Benthic cover was determined using mesophotic surveys (Pyle et al. 2016) conducted in areas 

east of Maui (Atlantis box 28) to get an indication what the percent cover is when Leptoseris and 

Montipora are present. We then used the predicted presence/absence of Leptoseris and 

Montipora maps for the entire inhabited Hawaiian Islands (Veazey et al. 2016) and estimated the 

percent cover by multiplying the likelihood estimate of Leptoseris/Montipora being present with 

the percent cover from the surveys. For turf algae, we assumed a cover of 1% at mesophotic 

depths. We further assumed that when substrate is suitable for Leptoseris growth, it is also 

suitable for crustose coralline algae (CCA) and multiplied the estimated Leptoseris cover with 

0.5 to get an assumed CCA cover (Rhodolithophores) at mesophotic depths. For macroalgae 

(e.g., Halimeda beds) we calculated the ratio of macroalgae to Leptoseris cover from PISC 

surveys, which was 1.25 for calcareous macroalgae and 1 for fleshy macroalgae, and multiplied 

the Leptoseris cover with these ratios to get an estimate of macroalgal cover.

Invertebrates 

Contributor: Elena Cosner

In the “twilight” zone, numerous species of structural corals and sponges foster rich and diverse 

ecosystems. We investigated community composition, abundance, and productivity of benthic 

macroinvertebrate communities along a depth gradient. Invertebrate data were obtained from 532 

submarine dives conducted by the Hawaiʻi Undersea Research Laboratory at the University of 

Hawaiʻi at Mānoa in depths of 30–400 m. From these surveys, we focused on macroinvertebrate 

composition across the depth gradient in the model area, seeking to understand distinct depth 

zones as it relates to benthic macroinvertebrates. 

A total of 17,367 observations of 462 species were analyzed using community ecology statistical 

methods. Using ArcMap point to line in WGS 1984 UTM Zone 4N projected coordinate system, 

the length of each submersible dive was determined in meters. Assuming a 3-m “view frame” 

from which the organisms were identified from the submersible, the total survey area was 

determined for each dive. For Mokoli’i dives, which were conducted before the advent of GPS, 

an average of the RCV dives was used as survey area. Any missing dive length data were filled 

in with average area from similar dives. From the area for each dive, the abundance per dive per 

square meter for each taxa was determined. Then, from the recorded depth, each observation was 

placed into a 10-m depth bin. Community structure was examined using the Bray-Curtis 

dissimilarity metric (also known as Sorenson index). Hierarchical clustering, using the clustsig 

package in R18 was based on the complete-linkage method, which maximizing the distance 

between clusters to visualize the faunal breaks along the depth gradient (Clarke et al. 2008). The 

SIMPROF (Similarity Profile) analysis, with an alpha = 10-7 (source), determined the most 

significantly different clusters used to evaluate which depth bins act as a distinguishable 

community. A dendrogram from the hierarchical clustering created a visual of the zonation of

depth in assemblage structure. The depth zones from the SIMPROF analysis were used with 

NMDS (non-metric multidimensional scaling) to visualize the community structure. 

 

18 https://cran.r-project.org/web/packages/clustsig/index.html
88

PERMANOVA (permutational analysis of variance; Anderson, 2017) identified the significance 

of the a posteriori depth zones based on SIMPROF results. Finally, SIMPER (similarity 

percentages) identified which species were responsible for the main differences within the depth 

zones.

Functional groupings for each species observed was determined from the existing literature 

based on diet, commercial importance, and habitat affinity (i.e., demersal vs pelagic). Many 

species did not have described diets and diet had to be inferred from species with similar 

lifestyles at similar depths. Due to the broad descriptions of the functional groups, related species 

are likely a good reference. 

We found four distinct invertebrate community clusters along the depth gradient from 30–409 m: 

30–89 m, 90–159 m, 160–279 m, 280–409 m. Two larger clusters, 30–159 m, and 160–409 m 

(Figure A5, Figure A6), were additionally more significantly different than the interior clusters. 

These two depth ranges can be broadly defined as the mesophotic and subphotic zones in 

Hawaiʻi, with differentiations between upper and lower zones for each zone with an associated 

assemblage of macroinvertebrates. 

Figure A5. Hierarchical cluster dendogram using Bray-Curtis dissimilarity analysis of 

invertebrate presence/absence between 30 and 409 m. The colored boxes indicate 

groups that have significantly (p < 0.00001) distinct faunal communities based on the 

analysis of Similarity Profiles (SIMPROF). The values on the x-axis represent 10-m depth 

intervals (e.g., 30 refers to the 30–39 m depth interval).
89

Figure A6. Non-metric multidimensional scaling (NMDS) ordination plot of faunal breaks 

along 10-m depth bins using a priori zones from the hierarchical clustering, 

(PERMANOVA; p<0.001). Colors represent distinct faunal depth zones. The values on the 

plot represent 10-m depth intervals (e.g., 30 refers to the 30–39 depth interval). The dark 

grey areas on the plot encompass the entire mesophotic (30–159 m) and subphotic (160–

409 m) zones.

In these depth ranges, filter feeding invertebrates (chiefly cnidarians and sponges) dominated, 

whereas abundance of mobile invertebrates was relatively low. The mesophotic zone was 

dominated by hard corals and the subphotic zone was defined by soft corals. As depth increases, 

organisms dependent on photosynthesis, such as macroalgae and zooxanthellate corals, decrease 

in density with the scarcity of light. A shift from light-dependent to light-independent habitat 

builders occurs, changing the composition of reefs along this depth gradient. In the mesophotic 

zone, cnidarians were dominated by Scleractinia (hard corals) and Antipatharia (black corals; 

Figure A7). On the contrary, in the subphotic, Alcyonacea (soft corals) and Antipatharia were the 

dominant taxa. Furthermore, the subphotic zone had a wider abundance across taxa groups. In 

the mesophotic, taxa Zoantharia (zoanthids), Pennatualcea (sea pens), Corallimorphia 

(hydrozoans), Actiniaria (anemones), and Ceriantharia (tube-dwelling anemones) were absent or 

were only minor constituents of the cnidarian assemblage, whereas in the subphotic zone, these 

taxa had relatively higher abundances (Figure A7). Upper and lower mesophotic zones were 

distinctly different mainly due to the high abundance of hard corals (Leptoseris sp. Montipora sp. 

Porites sp and Pocillopora sp.), black corals (Anthipastes sp.) and the invasive coral species 

Carijoa riisei in the upper mesophotic and of sea urchins and a species of black coral 

(Stichopathes sp.) in the lower mesophotic. Differences between the upper and lower subphotic 

zones can be mostly attributed to black corals being mostly dominant in upper subphotic and 
90

precious corals (e.g., the gold coral Kulamanamana haumeaae) abundant at lower subphotic 

depths.

Figure A7. Cnidarian family assemblage differences by relative abundance between the 

mesophotic (open bars) and subphotic (black bars) zone.

Considering only mobile invertebrates, sea urchins were the most abundant, contributing around 

14% of the mobile invertebrate relative abundance for both the mesophotic and the subphotic 

zones. Lobster were much more abundant in the mesophotic compared to the subphotic, 

contributing around 2% relative abundance, whereas in the subphotic, lobster rounded down to 

about 0%. Squat lobster, on the contrary, contributed to 7% of the relative abundance in the 

subphotic, and around 0% in the mesophotic. Shrimp, crabs, sea stars, and crinoids were as well 

more abundant in the subphotic compared to the mesophotic zone; however, sea cucumbers were 

more abundant in the mesophotic than the subphotic zone (Figure A8). 
91

. 

Figure A8. Comparison of mobile invertebrate relative abundance as a function of total 

invertebrates in the mesophotic (open bars) and the subphotic (black bars) zone.

Fishes

Fish abundance came from mesophotic surveys conducted by PIFSC (Asher et al. 2017, PIFSC 

unpublished data), Struhsaker (Struhsaker 1973), and the University of Hawaiʻi at Mānoa (UH) 

Drazen lab (Moore et al. 2016) and Hawaiʻi Underwater Research Lab (HURL). To allocate a 

biomass value to the MaxN values from the underwater baited camera surveys, we compared 

sites for which we had both shallow water coral reef surveys conducted by PIFSC19 and 

underwater camera surveys (Asher et al. 2017) to calculate conversion factors for each functional 

group. These conversion factors were then used to calculate biomass values. HURL data had 

records of 237 surveys with species counts binned in quantity categories. We assumed the “1 or 

more” category was equal to 3, “1–5” was 3, “6–10” was 8 and “>10” was 15 unless the notes 

specified a group size. Where possible we used the average length and length-weight parameters 

from PIFSC or Struhsaker (1973) and if not available we took 1/2 of the species-specific 

maximum length provided by fishBase20 and the reported length-weight conversion factors to 

estimate biomass for each species. Track length of HURL surveys were estimated in ArcGIS and 

 

19 https://www.pifsc.noaa.gov/cred/pacific_ramp.php 20 www.fishbase.org
92

multiplied with a field of view of 3 m to get survey area per survey. Where track lengths were 

absent we used a mean value from comparable dive durations. To get the total biomass estimate 

per functional group, we took the mean of the different survey estimates.

For the subphotic groups, data came from HURL, UH Drazen Lab, Struhsaker (1973), and 

PIFSC (PIFSC unpublished data). Struhsaker (1973) had data to calculate species-specific 

biomass per m2. HURL data had records of 432 subphotic surveys and we estimated biomass for 

the mesophotic surveys using length data derived from field surveys (Parrish 2006) or fishBase. 

We used the conversion factors calculated for the mesophotic data to adjust the UH and PIFSC 

BotCam data. To obtain the final biomass value we took a mean of the different data sources. 

Bottomfish biomass was excluded from the previous survey data and came from the PIFSC stock 

assessment group who conducted underwater baited camera surveys to estimate spatial 

distribution and stock size (Richards et al. 2016; Ault et al. 2018). 
93

Appendix F: Biomass of shallow prey fish and mesopelagic 

scatter layer

Contributors: Donald Kobayashi, Réka Domokos

Shallow prey fish biomass around the inhabited Hawaiian Islands was estimated from a multistep process incorporating unpublished aerial survey data, high-resolution bathymetry, State of 

Hawaiʻi commercial catch reports, dynamic production model stock assessment outputs, and 

numerous assumptions based on the published literature and expert opinion. Shallow prey fish 

were operationally assumed to be a coastal pelagic complex of 2 jacks (Carangidae): bigeye scad

(Selar crumenophthalmus), henceforth akule, and mackerel scad (Decapterus macarellus), 

henceforth opelu. Biomass estimates for akule and opelu were accomplished separately, as 

described below. 

Unpublished akule aerial survey data from collaborating scientific partner John Wiley (Poseidon 

Fisheries Research) from 2015 to 2016 was used to identify the location of akule schools around 

the island of O’ahu, and partnered fishing operations at the same time provided measures of 

akule school biomass. The survey swath of the aerial surveys was also calculated. Both the depth 

distribution of this survey area and the individual akule school locations were matched to a highresolution (50 m) bathymetry database providing an estimate of biomass per square kilometer of 

area within 1-m depth bins. A modeled biomass function was created using an additive 

composite of 2 logistic curves which were able to account for the shape of the function and its 

asymmetry. This depth dependent akule biomass function was then used to estimate akule 

biomass in each of the Atlantis boxes with an assumed linearly proportional allocation scheme to 

each box using the minimum and maximum depths for that box and its total area. The akule 

biomass estimates were then scaled to match a total biomass as estimated from a dynamic 

production model stock assessment which used State of Hawaiʻi commercial catch reports from 

1966 to 1997 (Weng and Sibert 2004). The assumed biomass is taken from their estimate of 

carrying capacity K using what they called the “scalar K” approach and a conservative estimate 

of current biomass of 0.5 K (304,587 kg), assuming that there may have been slight declines in 

biomass since the time of that stock assessment which showed biomass to be somewhere in the 

interval between 0.6 K and 0.8 K. An examination of commercial catch data subsequent to that 

time period shows a slight decrease in akule landings over the immediately subsequent 20 years 

but akule landings have been generally stable over a multi-decadal time period from 1948 to 

2018. The aerial biomass estimate was scaled by a factor of 4.5 to achieve a match to the Weng 

and Sibert (2004) biomass.

Opelu were assumed to occur at deeper depths than akule with a biomass function estimated 

from the shape parameters of the akule biomass function logistic curves and a depth offset. The 

opelu biomass function was further modified to incorporate a bimodal shape reflective of fisher 

and scientist observations of opelu schools in depths of 30–60 m and 90–120 m. These depths 

correspond to a day fishery and a night fishery, respectively. It is not well understood whether 

the fish move between the 2 areas over the course of a day/night or are spatially segregated based 

on size/age. Regardless, for purposes of Atlantis modeling a bimodal biomass function based on 

depth will capture the pertinent biomass patterns for this species as averaged over a block of time 

greater than or equal to 24 hours. The depth dependent opelu biomass function was then used to 

estimate opelu biomass in each of the Atlantis boxes in the same way as for the akule. 
94

Micronekton biomass in the mesopelagic scatter layer around the inhabited Hawaiian islands was 

estimated from active acoustic data collected on the leeward side of Oahu in 2004 (Domokos et 

al. 2010). Since data on micronekton is severely limited, biomass estimates from this data set 

were assumed representative for the entire modeled area. To obtain micronekton density 

estimates, acoustic backscatter strength (Sv., in dB re 1 m-1), proportional to density assuming the 

composition of organisms do not change (Simmonds and MacLennan 2008), was set at a 

threshold of -75 dB to contain only micronekton, such as small (2–20 cm) fish, squid, shrimp, or 

shrimp-like and gelatinous organisms. As micronekton form two ubiquitous horizontal scattering 

layers, Sv from the shallow (SSL) and deep scattering layers (DSL), were averaged and separated 

by day- and nighttime values. This step was necessary since most micronekton undergo diel 

migration from the daytime DSL to the nighttime SSL (Figure A9). 

To calculate biomass, mean length of micronekton was assumed to be 5 cm. This value was 

based on sizes obtained during simultaneous micronekton tow samples and the fact that they 

made up most of the biomass of organisms (Pakhomov et al. 2010). Target Strength (TS, dB re 1 

m2) estimates were calculated from a TS vs. length relationship for myctophids (Simmonds and 

MacLennan 2008), then used in combination with the mean Sv values to obtain densities in units 

of number per m2 per layer. Area densities were multiplied by the thickness of the layer (200 m) 

and by the area of the Atlantis box for biomass per box. Values only from the nighttime SSL 

were used since those values contained organisms that stayed in the SSL day and night and those 

that migrated to the DSL during daytime.

In lieu of an equation for weight and length relationship, the value of 1.82 g was used to convert 

the units from number to kg. This value was obtained as an average from tow samples collected 

in Hawaiian waters (John Denton, Florida Museum of Natural History, personal 

communication). Since the SSL, between 20 and 200 m depth, spread through all 3 Atlantis 

verticals depth layers (0–30, 30–150, and 150–400 m, respectively), values proportional to the 

thickness of the layers to that of the SSL were used. That is, for boxes with one, two, and three 

vertical layers, 15%, 75%, and 100% of the total biomass was used.

Figure A9. Acoustic backscatter during day (left panel), crepuscular (middle panel), and 

night (right panel) from the 2004 survey data. Red lines (>-47 dB) are backscatter from 

the ocean floor. The deep scatter layer can be seen between 450 m and 600 m during 

daytime (left panel), migrating upward around sunset, and in the 20–100 m layer during 

the night (right panel).
95

Appendix G: Allocation of Hawaiian monk seal biomass per model 

box

Contributor: Stacie Robinson 

We used a stepwise process to determine the biomass of Hawaiian monk seal of each age class 

occupying each of the Atlantis model boxes:

1. Estimate total population of Hawaiian monk seals in the main Hawaiian Islands. 

2. Allocate seals to age classes based on observed age distribution (based on size classes). 

3. Multiply biomass by numbers per age/size class according to mass estimated for each. 

4. Distribute biomass to boxes based on utilization distributions derived from satellite tags. 

1. Population estimate

We based total monk seal biomass estimates on the annual population estimates for the inhabited 

Hawaiian Islands (Baker 2016). From Kauaʻi to Hawaiʻi the population estimate represents a 

minimum count based on seal sightings reports made either by NOAA staff, organized volunteer 

groups, or the general public (minimum number only, no confidence range available). For 

Niʻihau the population estimate is based on beach counts with a correction factor for the 

proportion of seals expected to be hauled out and available for sighting on a given survey day 

(mean estimated with 95% confidence intervals, we used the mean here). We based biomass 

calculations on the median of the yearly estimates from 2013 to 2016.

2. Age distribution

We calculated a single age distribution for the whole model area (Niʻihau and Kauaʻi-Hawaiʻi 

not separated). Ages were based on the ages noted for all seals included in the survey data. We 

included animals with know, estimated, and minimum ages:

• Known age – animals tagged in the year they were born, so exact age is known;

• Estimated age – animals tagged after their birth year, but typically while still juvenile 

(based on animal size), so that birth year/age can be reasonably estimated;

• Minimum age – animals first seen as adults (based on animal size) have a known 

minimum age (i.e., at least 5 years old in the year first seen), but not a precisely 

estimated age.

The Atlantis model allows ten age classes which are based on a consistent interval. Thus, we 

divided the total population into ten 2-year age classes (0–1 yr, –... 18–19 yrs). We chose a 2-

year interval to mimic important natural patterns in the younger age classes; because immature 

seals are growing, we wanted narrow age classes so that mass estimates could accurately applied 

by class, also survival rates are most variable for youngest age classes. We recognize that monk 

seals are observed to live longer than 19 years; however, older seals would represent a very small 

proportion of the overall biomass, and there were 0 seals above 19 years old in the Hawaiʻi data 

used to calculate the age distributions. We multiplied the total population (median estimates) by 

the proportion in each age group to get the estimated numbers of animals per age group.
96

3. Biomass 

Biomass estimates for each age class were based on (Baker et al. 2014), and correspond to the 

estimates used for juveniles/subadults/adults in previous ecosystem modeling (Parrish et al. 

2011; Weijerman et al. 2017). Ages 0–1 yrs: 60 kg; Ages 2–3 yrs: 125 kg; Adults 5–6 yrs to 18–

19 yrs: 170 kg. 

Overall biomass per age group was calculated as:

Total population * proportion of population in age class X * estimated mass / seal of age class X

4. Utilization distribution

Monk seal biomass was allocated to model boxes within 400-m depth (within the typical 

foraging range of monk seals), deep water boxes far off shore (boxes 0, 7, 14, 15, 51, and 80) 

were considered transitory in the model, with biota passing through in transit to other boxes, but 

biomass and biological dynamics are not assigned to these boxes. All other boxes were assigned 

biomass of seals in proportion to the amount of time seals spent in those areas – according to 

satellite tracking data.

Our data set consisted of 24 seals tracked by Argos satellites for an average of 100 days. The 

study set included 17 males, 7 females; 12 were adults, 4 subadults, 5 juveniles, and 3 weaned 

pups. Tracked animals were captured and instrumented on Kauaʻi, Oahu, Molokaʻi, and Hawaiʻi. 

Animals were instrumented from 2004 to 2015 for foraging studies (Cahoon 2011; Wilson et al. 

2017) as well as opportunistically (e.g., for tracking after health interventions; NMFS 

unpublished).

Because circumpolar Argos satellite coverage is sparse in the tropics, and because a seal must be 

wet but at the surface to get a satellite fix, Argos-based locations represent an incomplete sample 

of an animal’s movement track. Thus, we used routines in the R package crawl to interpolate 

tracks, generating points at even intervals along each track (Johnson 2015). In this way, the 

number of points in a given area (e.g., Atlantis model box) can be used to represent the amount 

of its time an animal spent in that area. We calculated the proportion of each seal’s points in each 

of the Atlantis model boxes, and then used the average proportion of points per box as the 

measure of the percent of total seal biomass that should be allocated to that box.

A substantial portion of the Hawaiʻi seal population inhabits Niʻihau, and is seldom seen 

elsewhere. Because research effort is limited at Niʻihau (privately owned island), no animals 

have been instrumented or satellite-tacked from there. Thus, to characterize this important part of 

the population, we had to estimate areas occupied by the Niʻihau portion of the seal population.

Based on the number of seals estimated at Niʻihau, and the number of them seen on neighboring 

Kauaʻi is a given year, we estimated that Niʻihau seals spend 85% of their time in waters around 

Niihau, and 15% of their time around Kauaʻi. For the 15% of time around Kauaʻi, we allocated 

biomass in the same proportions that other Hawaiʻi seals used the boxes around Kauaʻi Island. 

For the 85% time allocated to Niʻihau boxes, we had to make our best guess, so we allocated 

time to boxes based on depth and area. We assumed seals would use boxes proportional to their

area for shallow areas that make up the most frequent dive depths for monk seals (30-m deep).
97

For deeper boxes, we assumed that seal use was proportional to the box area, but discounted by a 

factor of 0.83 for boxes 31–150 m deep, and discounted by a factor of 0.66 for boxes 151–400 m 

deep. 

Consumption rates and diet composition

Consumption 

Previous research estimated that seals require 1364 Kcal/kg/day, and in captivity this caloric 

need could be satisfied by 1.5 kg of herring per day (Olesiuk 1993). We doubled the 

consumption rate of captive seals to account for the effort of foraging, thus assuming that wild 

seals require 3 kg of fish per day to satisfy their caloric needs (Parrish et al. 2012). 

Diet Composition

Diet composition for Hawaiian monk seals varies considerably depending on the method of diet 

analysis (Table A4). For example, fatty acid analysis (Iverson et al. 2011) indicates that snapper 

and squid are major parts of the monk seal diet; however, fecal remains analysis (Heikoop et al. 

2000; Cahoon et al. 2013) found these to be minor components, with triggerfishes and 

crustaceans being among most frequent diet items. Both methods found tang and surgeonfishes 

to be important diet items. Each method is subject to its own biases. Fecal analysis is highly 

dependent on digestion rates, and items eaten closest to shore may be more detectable in feces 

collected on beaches. Fatty acid analysis depends on models that can be influenced by model 

assumptions and the prey library used to parameterize them (although we expect our prey library 

represented most species in the prey base at various depth levels (Iverson et al. 2011; Parrish et 

al. 2012), and either is unlikely to provide the true/complete picture of the monk seal diet. Thus, 

for the diet in the Atlantis model, we included all prey items detected in either the fecal or fatty 

acid diet analyses. We assumed that the monk seal diet could include up to the highest proportion 

suggested by either analysis method.
98

Table A4. Monk seal diet composition based on the method of diet analyses.

Family (Subgroup if available, FA) MAX% Diet

Minor Overall

Belonidae 0.06

Callionymidae (dragonet gurnard) 0.36

Cheilodactylidae 0.37

Xanthidae (pebble crab) 0.37

Lutjanidae (pink snapper) 0.41

Holocentridae (squirrellfish) 0.06

Acanthuridae (unicornfish) 0.49

Apogonidae 0.55

Polymixiidae 0.55

Diodontidae 0.61

Carangidae 0.67

Ammodytidae 0.60

Cirrhitidae 0.73

Kuhliidae 0.85

Pentacerotidae (armorhead) 0.87

Ophichthidae (cutthroat snake eel) 0.37

Bothidae (flounder) 0.55

Pomacanthidae (angel cardinalfish) 1.04

Ophidiidae 1.16

Chaetodontidae (butterfly forcep pennant) 0.61

Synodontidae (lizard snakefish) 1.52

Pandalidae (shrimp) 1.68

Kyphosidae 1.95

Important in Fecal Only

Crustaceans (several spp) 13.16

Scorpaenidae 2.35

Ostraciidae 2.91

Priacanthidae 2.44

Monacanthidae 2.63

Serranidae 7.30

Congridae 9.81

Labridae 11.82

Balistidae 16.95

Important in 

Fatty Acid 

O l

Lithodidae (box crab) 2.11

Percophidae (duckbill) 8.82

Ommastrephidae (squid) 11.01

Pentacerotidae (boarfish) 11.66

Lutjanidae (flower snapper) 23.35

Important in Fecal and

Fatty Acid

Tetraodontidae (toby) 2.50

Mullidae (goatfish) 3.53

Pomacentridae (sergeant) 2.68

Scaridae (parrotfish) 6.03

Holocentridae (soldierfish) 8.23

Muraenidae (moray) 10.81

Acanthuridae (tang surgeonfish) 11.40

Octopodidae (octopus) 17.67

Lutjanidae (squirrelfish snapper) 28.90
99

Appendix H: Mixed trophic impact analyses

Results of the mixed trophic impacts analyses (Figure A10) in the Ecopath model developed for 

the inhabited islands of Hawaiʻi (Weijerman et al. In press).

Figure A10. Mixed trophic impacts of impacted (columns) groups by impacting (rows) 

groups.

Red indicates negative impacts and blue positive impacts. The shading represents the value with harder 

colors representing higher values.
100

Appendix I: Calibration: No Fishing

For all the functional groups we show the trend line of the total biomass in the modeled area. The 

objective of this calibration exercise was to ascertain that no group becomes extinct or increases 

unrealistically. In the biomass plots the black line is the biomass trend and the dashed green line 

is the estimated biomass at carrying capacity or the initial biomass and is just for reference. For 

all vertebrate groups we also show the numbers and weight-at-age where the rainbow colors 

correspond to the age classes with red being age class 1 and violet age class 10. The objective 

was to have plausible numbers at the age classes (i.e., high numbers for the juveniles and low for 

the oldest age classes) and weight staying between 0.5 and 1.5 of the initial weight for each age 

class.
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103
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Appendix J: Validation with fixed mortality per gear type

Tables A5–7 provide the results of the skill assessments of the model to reproduce similar 

catches by gear type or fishery forcing the model with a fixed fishing mortality as observed 

catches. These tables are followed by the figures to visually compare the results.

Table A5. Results of 6 complimentary skill assessments of modeled vs. observed 

catches of targeted functional groups by gear type or fishery. Numbers in bold indicate a 

significance at p<0.05.

Gear/Fishery Group

Spearman

Correlation 

Average 

Error

Average 

Absolute 

Error

Mean 

Squared 

Error

Reliability 

index

Model 

Efficiency

BFcomm/rec BFB -0.005 15.125 15.125 15.478 1.694 -20.9

BFcomm/rec BFW 0.109 -33.031 41.329 45.479 1.561 -0.9

BFcomm/rec PIS -0.176 -1.201 1.607 2.285 1.636 -2628.1

BFcomm/rec UKU -0.669 -2.584 2.584 2.804 8.988 -711.6

Line PIS 0.205 -82.719 107.922 147.445 1.462 -1450.2

Line RBC 0.297 -62.752 63.649 81.132 2.180 -1305.4

Line RBR 0.486 -25.386 25.386 41.993 4.815 -21112.7

Line RGR -0.127 13.343 13.343 13.658 2.792 -3913.2

Line RLP 0.000 -3.590 4.458 6.959 1.970 -23.5

Line RPA -0.059 0.205 0.402 0.475 1.712 -377.4

Line SHP 0.371 -64.251 64.251 73.684 2.431 -1342.0

Line UKU 0.023 -42.422 42.422 48.274 2.853 -37.9

Net PIS -0.013 -3.314 3.805 5.066 1.542 -1935.0

Net RBC 0.739 -1.993 9.291 12.155 1.604 -152.7

Net RBR 0.371 -33.328 33.791 62.552 2.376 -2542.4

Net RGR 0.153 37.109 37.109 39.690 1.896 -2027.0

Net RLP -0.600 -3.400 3.473 4.045 2.140 -23.7

Net RPA -0.159 3.385 3.385 3.595 2.003 -542.8

Net SHP -0.615 -167.874 168.247 203.505 1.826 -414.3

Net UKU -0.143 -0.008 0.010 0.022 4.522 -652.6

other SHP -0.489 15.356 37.126 44.750 1.534 -56.1

other RBC 0.083 -0.340 0.456 0.558 1.589 -211.2

other RBR 0.074 0.172 0.661 0.891 7.658 -770.1

other RGR 0.522 14.661 14.661 14.777 4.695 -6112.1

other RLP 0.325 -0.960 1.126 1.670 2.632 -79.1

other RPA -0.710 -1.031 1.140 1.493 2.298 -5710.6

Spear PIS 0.222 -4.012 5.358 7.258 1.455 -1366.9

Spear RBC 0.383 -1.142 8.386 10.347 1.440 -107.8

Spear RBR 0.356 -13.012 13.012 21.749 3.202 -6191.6

Spear RGR -0.377 89.990 89.990 91.385 2.976 -4333.1

Spear RLP -0.550 -6.069 6.963 9.926 2.068 -30.0
125

Gear/Fishery Group

Spearman

Correlation 

Average 

Error

Average 

Absolute 

Error

Mean 

Squared 

Error

Reliability 

index

Model 

Efficiency

Spear RPA -0.333 20.460 21.739 24.652 1.696 -345.7

Spear SHP 0.346 -12.950 12.950 13.714 15.697 -135537.8

Spear UKU 0.133 -6.153 6.379 8.066 2.191 -16.7

Table A6. Results of 6 complimentary skill assessments of modeled vs. observed 

catches of non-targeted functional groups by gear type or fishery. Numbers in bold 

indicate a significance at p<0.05.

Gear/Fishery Group

Spearman 

Correlation

Average 

Error

Average 

Absolute 

Error 

Mean 

Squared 

Error

Reliability 

index

Model 

Efficiency 

BFcomm/rec SBC -0.778 1.482 6.921 7.275 2.635 -1786.3

BFcomm/rec SPL 0.275 0.137 0.166 0.187 2.375 -388.4

Line RBP 0.265 0.785 2.409 2.867 2.627 -4.8

Line ROF 0.093 -1.181 18.217 22.001 1.854 -624.9

Line RSP -0.164 -2.658 2.663 4.177 7.821 -7450.3

Line MBC -0.027 50.449 50.449 50.499 8.302 -6684.1

Line MBP -0.819 -0.132 0.404 0.512 1.465 -138.2

Line MPL -0.875 4.463 6.208 7.159 4.200 -305.3

Line SHR -0.152 -21.478 27.599 67.174 5.110 -21333.4

Net RBP -0.178 0.029 0.039 0.044 5.092 -9.7

Net ROF -0.121 0.080 0.312 0.362 1.870 -398.6

Net RSP -0.408 -0.314 0.314 0.382 5.924 -1583.0

Net SHR -0.615 0.001 0.065 0.148 7.450 -3090.5

Net MBC 0.711 40.999 40.999 43.641 2.658 -2967.4

Net MBP -0.153 0.135 0.135 0.139 14.897 -532.5

Net MPL 0.590 -3.575 3.575 4.734 5.152 -32463.9

other RBP 0.027 0.085 0.088 0.094 3.915 -6.6

other RSP -0.676 -0.034 0.036 0.055 5.551 -1986.2

other ROF -0.700 -2.059 2.059 3.198 7.194 -221566.9

other SHR -0.238 -0.172 0.175 0.261 6.451 -19501.0

other MBC 0.495 -12.731 13.575 21.339 2.216 -26689.2

other MBP 0.328 -0.362 0.405 0.611 5.031 -15152.4

other MPL 0.510 -3.135 6.467 7.977 1.492 -145.6

Spear RBP 0.450 9.342 9.342 9.903 5.012 -7.2

Spear RSP 0.597 -0.213 0.218 0.305 4.377 -1133.8

Spear ROF -0.067 -0.797 1.013 1.378 1.821 -1440.1

Spear MBC -0.044 53.598 53.598 53.764 11.846 -7050.5

Spear MBP 0.506 0.420 0.502 0.537 7.009 -368.8

Spear MPL -0.247 6.791 7.222 8.091 2.649 -260.9
126

Table A7. Results of 6 complimentary skill assessments of modeled vs. observed 

catches of invertebrate functional groups by gear type. Numbers in bold indicate a 

significance at p<0.05.

Gear/Fishery Group

Spearman 

Correlation

Average 

Error

Average 

Absolute 

Error 

Mean 

Squared 

Error

Reliability 

index

Model 

Efficiency 

Line CEP -0.054 0.820 1.513 1.812 3.280 -3619.3

Line LOB 0.239 -0.002 0.002 0.004 28.487 -499989.0

Line OCT 0.278 -0.271 0.273 0.490 34.588 -42231.0

Net BC 0.016 0.005 0.005 0.005 4.143 -23.8

Net LOB -0.027 -0.257 0.259 0.366 28.141 -4966813.3

Net OCT 0.317 -0.090 0.090 0.133 23.225 -12544.6

other BC 0.474 -8.553 9.692 12.144 2.042 -39.8

other BFF -0.831 -1.245 6.656 8.129 2.107 -341.3

other BG 0.231 -0.003 0.014 0.021 6.292 -155655.7

other BIV 0.623 0.425 0.501 0.528 6.118 -10923.4

other CEP -0.240 0.364 0.508 0.609 2.575 -2310.4

other LOB -0.314 -21.602 22.698 43.707 3.157 -37459.7

other MAF 0.066 2.549 4.134 4.769 1.828 -2278.0

other OCT -0.535 -4.797 4.797 5.038 9.180 -318.9

Spear LOB 0.233 3.364 3.524 4.035 1.655 -241.7

Spear BC 0.695 0.335 0.335 0.348 2.716 -13.4

Spear BIV 0.705 0.033 0.040 0.043 4.834 -10735.8

Spear CEP 0.243 -0.001 0.008 0.013 3.762 -40218.3

Spear OCT 0.609 25.098 25.098 29.673 2.587 -1.7
127

Figure A11. Comparison of observed (red dots) historical time series of hook and line 

catches per functional groups and modeled (blue line) catches with a fixed fishing 

mortality.
128

Figure A12. Comparison of observed (red dots) historical time series of net fishing

catches per functional groups and modeled (blue line) catches with a fixed fishing 

mortality.
129

Figure A13. Comparison of observed (red dots) historical time series of other gear types

catches per functional groups and modeled (blue line) catches with a fixed fishing 

mortality.
130

Figure A14. Comparison of observed (red dots) historical time series of commercial (or 

recreational) deep-water catches per functional groups and modeled (blue line) catches 

with a fixed fishing mortality.