Climate-induced primary productivity change
and fishing impacts on the Central North
Pacific ecosystem
Evan A. Howell1, Colette C.C. Wabnitz2, John P. Dunne3, and Jeffrey J. Polovina1
1
NOAA Pacific Islands Fisheries Science Center, Honolulu, HI 96822-2396, USA
2
Secretariat of the Pacific Community, Coastal Fisheries Science and
Management Section, B.P. D5, 98848 Noumea Cedex, New Caledonia
NOAA Geophysical Fluid Dynamics Laboratory, 201 Forrestal Road
Princeton,
NJ 08540-6649, USA
3
(808) 983-5306
Evan.Howell@noaa.gov
www.pifsc.noaa.gov
Electronic Supplementary Material 1. Parameter estimates for the HLFG1 Ecopath model
ESM Section 1: HLFG1 Ecopath model parameters
The following supplemental material provides background on the Ecopath
modeling software, as well as justification, documentation and references for the
biological information used to construct the Hawaii Longline Fishing Grounds
(HLFG1) Ecopath model. Section 1 of the supplemental material describes in
greater detail the climate model used in the study, as well as the methodology
used to create the Ecopath with Ecosim model including results and discussion
from the balanced Ecopath model and base 1991–2010 Ecosim run. Section 2 of
the supplemental material details the sources for the biological data for each of the
28 functional groups in the Ecopath model.
The following acronyms are used in the text: PIFSC = NOAA Fisheries
Pacific Islands Fisheries Science Center, SWFSC = NOAA Fisheries Southwest
Fisheries Science Center, GFDL = NOAA Geophysical Fluid Dynamics
Laboratory, TOPAZ = Tracers of Phytoplankton with Allometric Zooplankton,
SPC = Secretariat of the Pacific Community, WCPO = Western Central Pacific
Ocean, CPUE = catch-per-unit effort, B = biomass in t km-2, P/B = production to
biomass ratio in year-1, Q/B = consumption to biomass ratio in year-1, EE = the
1
dimensionless ecotrophic efficiency, F = fishing mortality, M = natural mortality,
Z = total mortality.
The NOAA GFDL models
The NOAA Geophysical Fluid Dynamics Laboratory (GFDL) prototype Earth
System Model (ESM2.1) is based on the successful CM2.1 coupled climate model
used in the IPCC 4th Assessment and is composed of separate atmosphere, ocean,
sea ice, and land models that interact through an online flux coupler (Delworth et
al. 2006). The ocean model has a resolution of 1° in latitude and longitude north
of 30°N, whereas south of 30°N the latitudinal resolution progressively becomes
higher, reaching 1/3° at the equator. A biogeochemical model [Tracers of
Phytoplankton with Allometric Zooplankton (TOPAZ)] is integrated into the
ocean model. TOPAZ includes all major nutrient elements (N, P, Si, and Fe) and
four classes of phytoplankton: three classes of large phytoplankton (> 5 μm
diameter) (i) diatoms, (ii) diazotrophs (nitrogen fixers), and (iii) all others, and a
single class of small (< 5 μm) phytoplankton (cyanobacteria and picoeukaryotes).
Growth rates are modeled as a function of variable chlorophyll:carbon (C) ratios
and are co-limited by nutrients and light. Photoacclimation is based on the Geider
et al. (1997) algorithm, extended to account for co-limitation by multiple nutrients
and including a parameterization for the role of iron in phytoplankton physiology.
Loss terms include zooplankton grazing and ballast-driven particle export.
Remineralization of detritus and cycling of dissolved organic matter are also
explicitly included (Dunne et al. 2005). Run in a historical mode in the North
Atlantic, TOPAZ has been demonstrated to reproduce phytoplankton bloom
dynamics in the SeaWiFS time-series, as well as the interannual variability over
the 50-year Continuous Plankton Recorder period, but has not captured fully the
regime shifts observed in that series (Henson et al. 2009a; Henson et al. 2009b).
In the North Pacific, TOPAZ captures the magnitude of the north–south
phytoplankton gradient fairly well, but the latitudinal location of the gradient
region from subpolar to subtropical is shifted south, because of a coupled ocean
atmosphere response as the ocean attempts to compensate for an overly cold
northern polar region by fluxing heat to the atmosphere with associated deep
winter convection. The latter tends to shift the subtropical gyre boundary to the
south in the western and central basins (Rykaczewski and Dunne 2010).
2
The TOPAZ model was incorporated in the coupled climate simulations with
biogeochemical parameters initialized from observations from the World Ocean
Atlas 2001 (Conkright and Boyer 2002) and Carbon Dioxide Information
Analysis Center (Key et al. 2004). The coupled climate model was spun up for
1000 years, with a fixed CO2 atmospheric boundary condition of 286 ppm. For an
additional 100 years, the atmospheric boundary condition was switched to a fully
interactive atmospheric CO2 tracer. Simulations were then made based on the CO2
trajectory described by the A2 scenario from the IPCC Special Report on
Emission Scenarios (SRES; (Nakicenovic et al. 2000), where atmospheric CO2
increases continuously from ~ 370 ppm in 2000 to 850 ppm in 2100.
To create the time series used to drive the Ecosim climate scenarios, the
study area was modified to have a dynamic northern boundary based on sea
surface temperature to account for seasonal and long-term changes in the
ecosystem. The Hawaii longline fishery exhibits strong north-south seasonality,
targeting swordfish at thermal fronts in winter months (Seki et al. 2002). The fleet
targets tunas later in the year over a broader region reaching as far north as the
thermal fronts used in targeting swordfish during the winter (Howell et al. 2010).
There is a large spatial gradient in model phytoplankton size class distribution,
with a greater concentration of large phytoplankton to the north. To capture the
seasonal north-south dynamics of the phytoplankton and the areas that the fishery
targets, a dynamic northern boundary was defined as the geographic location of
the 17°C (Fig. 1). Annual time series of the large and small phytoplankton groups
were calculated over the study area using this dynamic northern boundary from
1991 to 2100 using the Ferret Data Visualization and Analysis environment1.
The HLFG1 Ecopath model
Ecopath with Ecosim allows the user to present an analysis on mass balance
model parameters and structure at time t (Ecopath) (Christensen and Pauly 1993)
and also incorporates a time dynamic aspect of ecological systems (i.e., attempts
to predict ecosystem structure and function at time t+1 (Ecosim)) (Walters et al.
1997). Data required for each functional group includes a minimum of three out of
four basic biologic parameters, a diet matrix representing the predator/prey
relationships in the ecosystem, and fishery landing/bycatch information if
1
http://ferret.pmel.noaa.gov/Ferret/home
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applicable (catch). The biologic parameters include: a biomass estimate (B); a
production to biomass term (P/B) that in most circumstances is equal to the sum
of fishing and natural mortality (i.e., total mortality, Z= F + M); a consumption to
biomass term (Q/B); and an “ecotrophic efficiency” term (EE), which is defined
as the fraction of production that is consumed within the system or removed by
fishers. Units of the model are expressed in t km-2 yr-1 wet weight for flows and t
km-2 wet weight for biomasses. Production per unit biomass (P/B) and
consumption per unit biomass (Q/B) have the dimension yr-1.
The main model structure of the model including most of the Ecopath
functional groups and initial biological parameters was adapted from the CNP8
model used in Cox et al. (2002a) and updated and adapted for use in Essington
(2006). All biomass parameters for species/groups were estimated as close to
1991 as possible. Although based on the CNP8 model framework (Cox et al.
2002a), the current model focused solely on the Hawaii longline fishing grounds
and was updated to include more recently available biologic and fishery
information where possible. Additionally, HLFG1 includes a number of different
functional group aggregations for mid-trophic level and mesopelagic species and
two different size-class phytoplankton and zooplankton groups. We also chose to
link the four adult and juvenile tuna species groups via “multi-stanza”, the way in
which Ecopath represents model groups that have complex life histories and
selective harvesting of older animals.
To balance the model, changes were first made to the diet matrix, as diet
compositions represent only snapshots of the feeding habits of individual species
and are likely to be relatively variable based on location and time periods of data
collection. The model required only minor adjustments and was considered
balanced when: (1) The model produced realistic ecotrophic efficiencies (EE< 1);
and (2) values of the production to consumption ratio (P/Q) for functional groups
ranged between 0.05 and 0.35, with the exception of groups with fast growth rates
(higher ratios), and top predators (lower values) (Christensen et al. 2008).
Model fitting, uncertainty and sensitivity testing
An initial Ecosim scenario for the 1991–2010 time period was built on the
balanced Ecopath model to tune the model and derive vulnerability parameter in
Ecosim. Vulnerability is one of the most important parameters in Ecosim, and
represents the degree to which a large increase in predator biomass will affect
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predation mortality for a given prey. A low vulnerability (close to 1) means that
an increase in predator biomass will not cause a noticeable increase in predation
mortality. Conversely, a high vulnerability parameter (e.g., 100) indicates that an
increase in predator biomass will increase the predation mortality for a given prey.
Ecosim does not include complete formal sensitivity analyses to test the effect of
all input parameters on model outputs. The Monte Carlo routine, however, can be
used to examine uncertainty in the Ecopath input parameters (Christensen and
Walters 2004). Specifically, the routine was used to search for a better fit to timeseries data, drawing parameters from set ranges based on the earlier defined
pedigree confidence intervals. Only time series for key groups caught by the
Hawaii longline fishery were included to estimate the sums of squared residuals
statistic (SS). Five-hundred Ecosim simulations (each involving up to several
thousand iterations to find a balanced model) were conducted by the Monte Carlo
search routine. The routine was unable to find any initial permutation of input
parameters that resulted in lower sum of squares residuals than we obtained
through the Fit to Time Series procedure (Christensen et al. 2009).
Ecopath model balancing and initial Ecosim model fit scenario (1991-2010)
Trophic parameters for the HLFG1 Ecopath model are listed in ESM Table 1,
with model-estimated parameters in bold. Primary producers represented 23% of
the total biomass, and apex predators (e.g., sharks, tunas and billfish) less than 1%
of the total biomass. ESM Figure 1 illustrates the food web and trophic flows for
the HLFG1 model. Major trophic pathways occurred either through the
invertebrate or epi-fishes groups. The Ecosim model fit the biomass and CPUE
time series well for the mid-trophic and apex functional groups, capturing the
decline in tuna and billfish species, and concurrent rise in blue sharks and midtrophic level species (ESM Figs. 2a-h). The 95% confidence intervals constructed
using the Monte Carlo simulation show that modeled predictions for most species
were not towards either extreme of the 500 runs (ESM Figs. 2a-h). Modeled catch
for the Hawaii longline fleets fit the reported landings from federal logbooks for
the year 2010 (r2 = 0.99, p < 0.01).
Ecosystem model structure discussion
Ecosystem food web models represent powerful tools to look at a system’s
complex reactions to perturbations and interactions between species. As with all
models however, being simplifications of the natural world, they have limitations
5
and provide a possible set of outcomes rather than a definitive answer. One
important assumption of mass-balance models such as Ecopath is that they
represent an instantaneous ‘snapshot’ of the state of an ecosystem that is assumed
to be in equilibrium (Christensen and Pauly 1993). In this study, we chose the
year 1991 as the equilibrium point for Ecopath as this was the first complete year
available for high quality logbook data for the Hawaii longline fishery. A
potential problem is that a large amount of fishery removals had taken place prior
to 1991, and the results of several studies suggest that the biomass of many
species was already in severe decline by that point in time (Cox et al. 2002b;
Sibert et al. 2006). By balancing the model in 1991 we lose the dynamics from the
fishery prior to this period, but as we were concentrating on the effects of climate
change to the Hawaii longline fishery we chose to fit the Ecosim model to the
time period with a consistent fishery time series (1991–2010). The model fit the
data well for this period, lending confidence to findings of simulations into the
future.
Ecopath and Ecosim results are affected by the quality of data available. This was
impetus to aggregate a number of species into functional groups or guilds, as
sufficient information was lacking to parameterize them adequately as individual
species within Ecopath. A prime example of this was the decision to create a large
“invertebrates” group, rather than specify components of this group such as
jellyfish. It is understood that jellyfish dynamics are important in many
ecosystems (Pauly et al. 2009), yet adequate biological information (e.g., diet,
turnover rates) is lacking at the scale of the study area to lend confidence to any
model outputs for, or linked to, that group. The final Ecopath model pedigree was
0.384, around the midpoint of the range reported for other Ecopath models
(Morrissette 2007).
ESM Section 2: HLFG1 Ecopath model parameters
This section of the supplemental material details the sources for the biological
data for each of the 28 functional groups in the Ecopath model. These data formed
the biological input parameters in the Ecopath model including biomass,
production, consumption, “ecotrophic efficiency”, and a predator-prey diet matrix.
For a number of species biomass estimates were informed by data from
stock assessments, for which the geographic area is considered generally the
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entire WCPO. Within this overall area, each assessment adopts a region based
spatial stratification methodology. Biomass estimates were taken from the regions
in each assessment that covered the study. The remaining biological input
parameters were modified either with information from the literature, internal
studies and reports, calculated using parameters available from FishBase, or taken
from existing ecosystem models in similar regions of the Pacific. Calculation of
P/B and Q/B values followed Pauly (1980) and Palomares and Pauly (1989) with
mean temperature values for each calculation listed. P/B values calculated using
FishBase were checked against fishing mortality estimates if available, to ensure
that the total mortality P/B value was realistic and larger than reported F values.
EE values were either taken from existing models or estimated by the model. The
following list below contains the data sources used to obtain the final biological
parameters required in the model, listed by functional group. Some initial values
were adjusted during the model-balancing phase. As stated in the main text,
changes were first made to the diet matrix, as diet compositions represent only
snapshots of the feeding habits of individual species and are likely to be relatively
variable based on location and time periods of data collection.
Ecopath input parameters by functional group
Blue sharks (Prionace glauca)
Biomass was estimated from the most recent blue shark assessment in the North
Pacific (Kleiber et al. 2009). P/B and Q/B were calculated from FishBase
assuming a mean temperature of 21°C. F was taken from Kleiber et al. (2009).
Diet is from several publications including Seki (1993), Nakano and Seki (2003),
Kubodera et al. (2007), Júnior et al. (2009), and Lopez et al. (2010).
Other sharks
Representative species for this group included white sharks (Carcharodon
carcharias), tiger sharks (Galeocerdo cuvier), shortfin Mako sharks (Isurus
oxyrinchus), silky sharks (Carcharhinus falciformis), oceanic white tip
(Carcharhinus longimanus), Galapagos shark (Carcharhinus galapagensis),
bigeye thresher (Alopias superciliosus), common thresher (Alopias vulpinus),
pelagic thresher (Alopias pelagicus), sandbar shark (Carcharhinus plumbeus),
longfin mako (Isurus paucus), dogfish spp. (Squalus mitsukurii, Squalus
blainville), and crocodile shark (Pseudocarcharias kamoharai). P/B and Q/B
calculations were taken as the averages from Fishbase and weighted according to
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individual species’ assumed relative contribution to the group. F was taken as the
sum for large sharks and brown sharks from Cox et al. (2002a). Diet information
was based on information published in Strasburg (1958), Lopez et al. (2009),
Moteki et al. (2001), Cox et al. (2002a), Preti et al. (2004), Kubodera et al. (2007),
and Cabrera-Chávez-Costa et al. (2010).
Swordfish (Xiphias gladius)
Biomass data are from Courtney (PIFSC, pers. comm.), while P/B and Q/B were
calculated from FishBase using a temperature of 21°C. F is from Courtney
(PIFSC, pers. comm.). Diet was informed by values in Cox et al. (2002a), Moteki
et al. (2001), Markaida and Hochberg (2005), Chancollon et al. (2006) and
Watanabe et al. (2009).
Blue marlin (Makaira nigricans)
Biomass were taken from Kleiber et al. (2003), while P/B and Q/B parameters
were taken from FishBase using a temperature of 24°C. EE was taken from Cox et
al. (2002a). F is from Kleiber et al. (2003). Diet was informed by values in Cox et
al. (2002a), Brock (1985), Shimose et al. (2006), and Abitia-Cardenas et al.
(1999).
Striped Marlin (Tetrapturus audax)
Biomass was informed by data in Piner (PIFSC, pers. comm.). P/B and Q/B
values were taken from FishBase using a temperature of 24°C. F is from Piner
(PIFSC, pers. comm.). Diet data were taken from the published and grey literature
(Abitia-Cardenas et al. 1997; Moteki et al. 2001; Cox et al. 2002a).
Other Billfish
This functional group includes sailfish (Istiophorus platypterus), black marlin
(Makaira indica), and shortbill spearfish (Tetrapturus angustirostris). P/B and
Q/B were calculated from FishBase using a temperature of 24°C. The EE value
was informed by Cox et al. (2002a), yet lowered to 0.4 in the final model to
reflect the removal of striped marlin. Diet information was informed by Cox et al.
(2002a), Rosas-Alayola et al. (2002); Arizmendi-Rodríguez et al. (2006); Shimose
et al. (2008). and Shimose et al. (2008).
Small Billfish
This functional group represents juvenile sized specimens of all billfish species,
but was not linked to “other billfish” through multi-stanza. P/B and Q/B values
8
were calculated from FishBase using a temperature of 24°C, while diet values
were informed by Allain (2005b).
Multi-stanza tuna groups
The Hawaiian longline fishery targets all tuna species except skipjack. All tuna
species included in the model consisted of two groups, adults, and juveniles.
These are linked via delay-difference age/size structured equations (multi-stanza),
which is the way in which Ecopath represents model groups that have complex
life histories and selective harvesting of older animals. The procedure requires
baseline estimates of Z (equivalent to P/B) and diet composition for each stanza,
but biomass, Q/B, and biomass accumulation BA (if applicable), for one ‘leading’
stanza only. Further, the B and Q/B for all stanza-groups besides the leading
(entry) stanza are calculated before entry to Ecopath, using the assumptions that:
body growth for the species as a whole follows a von Bertalanffy growth curve
with weight proportional to the length-cubed, and that the species population as a
whole has had relatively stable mortality and relative recruitment rate for at least a
few years, and therefore has reached a stable age–size distribution (Christensen
and Walters 2004). Multi-stanza parameters (K, age at maturity and age at
recruitment into the adult/harvested class) were taken from the most recent SPC
stock assessments (Hampton 2000; Langley and Hampton 2008; Harley et al.
2009; Langley et al. 2009; Holmes 2011). Juvenile fishing mortality was taken
from values calculated in the most recent stock assessments for the tuna species at
the time of writing (Langley and Hampton 2008; Harley et al. 2009; Langley et al.
2009; Holmes 2011).
Yellowfin (Thunnus albacares)
Adult biomass was taken from the stock assessment of Langley et al. (2009). The
natural mortality rate was found to be strongly variable with size, with the lowest
rate of around 0.6 - 0.8 yr-1 being for preadult yellowfin of between 50 and 80 cm
FL (Hampton 2000). P/B was taken from the sum of natural and fishing
mortalities from Langley et al. (2009). Q/B was calculated from FishBase using a
temperature of 24°C. Diet data were informed from a number of published and
grey literature, including Brock (1984); Moteki et al. (2001); Cox et al. (2002a);
Allain (2005a); and Graham et al. (2007). The von Bertalanffy growth parameters
used in the multi-stanza calculations were taken from Langley et al. (2009) and
Hampton (2000).
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Albacore (Thunnus alalunga)
Biomass was taken from the 2011 Stock Assessment of albacore tuna in the North
Pacific Ocean report (Holmes 2011), while P/B and Q/B were calculated from
FishBase using a temperature of 21°C. Fishing mortality was obtained from
Holmes (2011). The von Bertalanffy growth parameters used in the multi-stanza
calculations were taken from Holmes (2011). Diet information was taken from the
CNP8 model (Cox et al. 2002a) and updated with values from the literature
(Allain 2005a; Glaser 2009).
Bigeye (Thunnus obesus)
Biomass values were taken from Harley et al. (2009). P/B values were taken from
Harley et al. (2009) and Hampton (2000) based on natural mortality and an F
value of 0.2 taken from run 10 of Harley et al. (2009). Q/B was calculated from
FishBase using a temperature of 21°C. The von Bertalanffy growth parameters
used in the multi-stanza calculations were taken from Hampton (2000). Diet
information was taken from the CNP8 model (Cox et al. 2002a) and updated with
values from the literature (Moteki et al. 2001; Bertrand et al. 2002; Allain 2005a).
Skipjack (Katsuwonus pelamis)
Biomass was taken from Langley and Hampton Langley and Hampton (2008).
P/B was calculated using the natural and fishing mortalities from Langley and
Hampton Langley and Hampton (2008). Q/B was calculated from FishBase using
a temperature of 24°C. The von Bertalanffy growth parameters used in the multistanza calculations were taken from Hampton (2000). Diet information was taken
from the CNP8 model (Cox et al. 2002a) and updated with values from the
literature (Tanabe 2001; Allain 2005a).
Mahi mahi (Coryphaena hippurus)
P/B and Q/B were calculated from FishBase using a temperature of 24°C. EE was
taken from Cox et al. (2002a). Diet was informed by the literature including
Oxenford and Hunte (1999); Moteki et al. (2001); Olson and Galvan-Magana
(2002); Allain (2003).
Lancetfish (Alepisaurus ferox)
P/B was taken from Cox et al. (Cox et al. 2002a), while Q/B was calculated from
FishBase using a temperature of 18°C. Fishing mortality was estimated as 0.3
(McCracken, pers. comm.). The EE value for all lancetfish as well as mid-trophic
level fish was set to the level of the mahi group for consistency among all groups
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at this trophic level (mahi, lancetfish, and mid-trophic level fish). Diet information
was taken from the CNP8 model (Cox et al. 2002) and updated with values from
recent studies and the literature (Moteki et al. 2001; Allain 2003).
Mid-trophic level fish
This trophic guild was represented by numerous species including wahoo
(Acanthocybium solandri), snake mackerel (Gempylus serpens), pomfrets (e.g.
Brama japonica, Taractichthys steindachneri, Taractes rubescens and Eumegistus
illustris), opah (Lampris guttatus), escolar (Lepidocybium flavobrunneum ,
Ruvettus pretiosus, and Scombrolabrax heterolepis), as well as mola (e.g., Mola
mola and Ranzania laevis). P/B values for snake mackerel, escolar, and pomfrets
were taken from Polovina et al. (2009). The remaining P/B and Q/B values were
calculated for all species from FishBase using a suitable temperature value for
each species (17°–24°C). The final P/B and Q/B values were calculated based on
the average P/B and Q/B values weighted by catch numbers estimated by the 2007
National Bycatch report (PIFSC, McCracken, pers. comm.). The EE was set at 0.6
for consistency among all groups at this trophic level (mahi, lancetfish, and midtrophic level fish). Diet information was taken from existing ecosystem models
(Cox et al. 2002a; Olson and Watters 2003), as well as from available studies in
the literature (Yoshida 1973; Manooch III and Hogarth 1983; Nakamura and Parin
1993; Seki and Bigelow 1993; Allain 2003; Franks et al. 2007).
Epipelagic Fish
The epipelagic fish group represented smaller fish whose main habitat was in the
0–200m depth range. These included flying fish (exocoetidae), small tunas (e.g.
Auxis spp.), jacks (carangidae), and rainbow runner (Elagatis bipinnulata). B
follows an estimate from E. Pakhomov (pers. comm.). P/B and Q/B values were
calculated for all species from FishBase using a suitable temperature value for
each species (17°–24°C). The average P/B and Q/B were used as the final values.
Diet information was taken from existing ecosystem models and adjusted during
the balancing phase (Cox et al. 2002a; Olson and Watters 2003; Essington 2006).
Invertebrates
The invertebrate group represented various biologic groups including crustaceans
and gelatinous organisms larger than the mesozooplankton size threshold of 5000
µm). Examples in this group included: Stomatopoda, Hyperiidea, Amphipoda,
Palinura, Enoplometopidae, Phronima sp., Thalassocaris sp., Scyllaridae,
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Harpiosquillidae as well as pyrosomes, cnidarians, and ctenophores. Initial P/B
and Q/B were taken from the micronekton groups of Cox et al. (2002a), with final
values adjusted during model balancing. An EE of 0.8 was chosen to reflect their
high consumption within the ecosystem. Initial diet information was taken from
the micronektonic groups in existing ecosystem models (Olson and Watters 2003;
Allain et al. 2007).
Epipelagic mollusks
This mollusks group represented the larger squid that migrate through depth and
feed within the epi- and mesopelagic depth ranges. We have chosen to label these
mollusks “epipelagic” to avoid naming confusion, although mollusks in this group
undergo diel vertical migration between depth zones (e.g. Ommastrephidae). This
group includes: Ommastrephes bartramii, Stenoteuthis oulaniensis, Eucleoteuthis
luminosa, Hyaloteuthis pelagica, Moroteuthis lonnbergi, Onychoteuthidae,
Argonautidae, Carinariidae, Cavoliniidae, Loliginidae, Sepiolidae,
Thysanoteuthidae. Biomass estimates were taken from available data and the
literature, and the average biomass estimate was used in the model (Nikol’sky
1988; Pinchukov 1989). P/B was estimated from natural mortality studies (Murata
and Shimazu 1982; Ichii et al. 2006; Chen 2010). Q/B was taken as the average of
estimates from the literature and existing models (Brodeur et al. 1999; Cox et al.
2002a; Essington 2006). Initial diet information was taken from the literature
(Seki 1993) and existing ecosystem models (Cox et al. 2002a; Olson and Watters
2003; Essington 2006).
Mesopelagic Fish
The mesopelagic fish group represented smaller fish with a daytime depth habitat
from 200m to around 1000m. Many of the species in this group undergo diel
vertical migration (e.g. Myctophidae). Representative fish in this group included:
Myctophidae, Bathylagidae, Gonostomatids, Photichthyids, Sternoptychids, and
Melamphaids. The biomass estimate used was from a study of nekton in the
Pacific (E. Pakhomov pers. comm), which is within the biomass range for the
mesopelagic fish and mesopelagic micronekton groups of the CNP8 model (Cox
et al. 2002a). P/B, and Q/B were taken from Essington (2006) and slightly
adjusted during model balancing. Diet information was taken from existing
ecosystem models (Cox et al. 2002a; Olson and Watters 2003; Essington 2006).
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Mesopelagic mollusks
This grouping contains the smaller squids that may undergo diel vertical
migration but are thought to spend more time at depth. Examples included here
are: Enoploteuthidae, Pyroteuthidae, Amphitretidae, Histioteuthidae, Gonatopsis
spp., and Chiroteuthis spp. Initial biomass estimates were taken from trawl
surveys (E. Pakhomov pers. comm.), and the small squids group in Essington
(2006) where maximum mantle length is < 30 cm (e.g. brachioteuthidae,
enoploteuthidae, pyroteuthidae). The P/B and Q/B were taken from the small
squid group of Essington (2006), with the P/B term raised by 15% during model
balancing. Diet information was taken from existing ecosystem models (Cox et al.
2002a; Olson and Watters 2003; Essington 2006).
Bathypelagic fish
The bathypelagic fish group included fish that spent most of their time below
400m range and may or may not undergo diel vertical migration. Examples of fish
included in this group included: Sternoptychidae, Gonostomatidae,
Trachipteridae, Oplophoridae, Gonostomatidae and Stomiidae. P/B and Q/B were
taken from the mesopelagic fish group of Essington, which contained several of
the fish families contained in this group. Diet information was modified from the
mesopelagic fish group to reflect their predation on organisms within deeper
habitat in the water column.
Mesozooplankton
We considered two zooplankton groups, defined based on size; small
(microzooplankton: 2–200 µm) and large zooplankton (mesozooplankton: 200 –
5000 µm). The mesozooplankton group included: copepods, which were
considered the dominant taxa, (Steinberg et al. 2008)(Neocalanus robustior,
Pleuromamma xiphias, Euchaeta rimana, and Oithona spp), Chaetognaths,
smaller gelatinous predators (medusae and siphonophores), pteropods,
appendicularians, amphipods, and euphausiids (Landry et al. 2001; Hannides et al.
2009). Biomass was derived from the University of Hawaii (UH) time series at
Station Aloha north of Hawaii. No data was available for 1991, so the average of
the 0–175m depth-integrated wet weights from 1994 were used. P/B was taken
from estimated annual mesozooplankton production at HOT (Roman et al. 2002).
The Q/B was estimated based on studies within the study area (Landry et al.
13
2008). Diet was based mainly on copepods, which accounted for over 75% of all
animals > 0.2 mm (Landry et al. 2008).
Microzooplankton
The microzooplankton group contained organisms within the < 2–20 m and >
20m size categories of the UH Station Aloha time series. Organisms in this
group included: ciliates, copepod nauplii, heterotrophic dinoflagellates, protozoa,
and tintinnids. Biomass was calculated from the UH epifluorescence microscopy
time series data (2004–2008). The average value for all samples from 2004 were
taken for each size class and depth sampled then summated across size classes.
This total value was integrated through the water column (0-175 m). The P/B –
term was taken from Landry and Calbet (2004), who estimated that growth rates
for microzooplankton are comparable to those of the phytoplankton community
they feed upon. We therefore set the initial P/B term equal to the P/B term of the
small phytoplankton group. This estimate was slightly adjusted during the model
balancing phase. The Q/B term was taken from Verity et al. (1996).
Microzooplankton diet was taken from available information in the literature (Le
Borgne and Landry 2003; Landry and Calbet 2004; Landry et al. 2008).
Large phytoplankton
The phytoplankton groups represent the partitioning of the four phytoplankton
time series produced by the GFDL TOPAZ model. These four phytoplankton
groups were put into two Ecopath groups, and two groups of primary producers
were included in the model based on size classification. Here the large
phytoplankton group contains autotrophs > 5 µm, mostly diatoms (TOPAZ group
i), diazotrophs (nitrogen fixers, TOPAZ group ii), and all others (TOPAZ group
iii). The biomass of the large phytoplankton group was taken as the sum of these
three model groups for 1991. The P/B term was calculated by the primary
production values used in the TOPAZ model integrated over 0–200m.
Small phytoplankton.
The small phytoplankton group represented autotrophs < 5 µm, including
cyanobacteria (Prochlorococcus, Synechococcus) and picoeukaryotes. As with the
large phytoplankton group, the biomass was taken as the sum of these three model
groups for 1991. The P/B term was calculated by the primary production values
used in the TOPAZ model integrated over 0–200m.
14
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ESM Table 1. Ecopath functional groups and basic input parameters. B is biomass (t km-2), where
(SA) indicates that a stock assessment was available, and (CPUE) indicates that catch-per-unit
effort data was used, P/B the production/biomass ratio (year-1), Q/B the consumption/biomass ratio
(year-1), EE is the (dimensionless) ecotrophic efficiency. Numbers in bold indicates that the
parameter was estimated as part of the mass-balance calculations of Ecopath, ‘–’ indicates that the
parameter is not defined.
ESM Table 2. Diet composition matrix for the Hawaii longline fishing grounds (HLFG) model.
20
Figure Legends
ESM Figure 1. Ecopath modeled food web of the Central North Pacific HLFG1 ecosystem.
ESM Figure 2. Fits of model predicted biomass (black lines) against biomass values from stock
assessments or CPUE trends from federal observer logbooks (grey circles) for a) swordfish, b)
striped marlin, c) bigeye tuna, d) yellowfin tuna, e) blue shark, f) mahi mahi, g) lancetfish, and h)
mid-trophic level fish. Thin grey lines represent the 95% confidence intervals generated from
Monte Carlo simulations in Ecosim.
21