Hypotheses of Spatial Stock Structure in Orange Roughy
Hoplostethus atlanticus
Inferred from Diet, Feeding,
Condition, and Reproductive Activity
Matthew R. Dunn*, Jeffrey S. Forman
National Institute of Water and Atmospheric Research Limited, Wellington, New Zealand
Abstract
We evaluate hypotheses for meso-scale spatial structure in an orange roughy (Hoplostethus atlanticus) stock using samples
collected during research trawl surveys off the east coast of New Zealand. Distance-based linear models and generalised
additive models were used to identify the most significant biological, environmental, and temporal predictors of variability
in diet, proportion of stomachs containing prey, standardised weight of prey, fish somatic weight, fish total weight, and
reproductive activity. The diet was similar to that observed elsewhere, and varied with ontogeny, depth, and surface water
temperature. Smaller sized and female orange roughy in warmer bottom water were most likely to contain food. Fish
condition and reproductive activity were highest at distances more than 20 km from the summit of the hills. Trawl survey
catches indicated greater orange roughy densities in hill strata, suggesting hill habitat was favoured. However, analyses of
feeding, condition, and reproductive activity indicated hill fish were not superior, despite fish densities on hills being
reduced by fishing which, in principle, should have reduced intra-specific competition for food and other resources.
Hypotheses for this result include: (1) fish in relatively poor condition visit hills to feed and regain condition and then leave,
or (2) commercial fishing has disturbed feeding aggregations and/or caused habitat damage, making fished hills less
productive. Mature orange roughy were observed on both flat and hill habitat during periods outside of spawning, and if
this spatial structure was persistent then a proportion of the total spawning stock biomass would remain unavailable to
fisheries targeting hills. Orange roughy stock assessments informed only by data from hills may well be misleading.
Citation: Dunn MR, Forman JS (2011) Hypotheses of Spatial Stock Structure in Orange Roughy Hoplostethus atlanticus Inferred from Diet, Feeding, Condition, and
Reproductive Activity. PLoS ONE 6(11): e26704. doi:10.1371/journal.pone.0026704
Editor: Howard Browman, Institute of Marine Research, Norway
Received June 29, 2011; Accepted October 2, 2011; Published November , 2011
Copyright: ß2011 Dunn, Forman. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The research trawl surveys were funded by the New Zealand Ministry of Fisheries. The analyses and preparation of the manuscript were funded by
National Institute of Water and Atmospheric Research project CF113511. The funders had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: m.dunn@niwa.co.nz
Introduction
Orange roughy is a long-lived, low productivity, vulnerable
deep-sea fish that has been targeted by industrial deep-sea trawl
fisheries worldwide [1–3]. The productivity of orange roughy is
thought to be one of the lowest of all exploited fishes, a consequence
of a longevity that may exceed 100 years, and maturity that may
not occur until 20–40 years of age [1,2]. Catch rates in most orange
roughy fisheries declined rapidly following exploitation, and as a
result orange roughy fisheries have been closed around Europe,
Chile, Southern Africa, Australia and New Zealand. The only
large-scale commercial fisheries remaining for orange roughy in
2011 are around New Zealand, with the most recent annual catch
being about 9200 t, even though many of the New Zealand stocks
have been depleted (biomass fished down to below 20% of initial
levels [4]). As of 2011, the largest stock, on the east and south
Chatham Rise, has collapsed and is apparently continuing to
decline despite catch reductions; the stock on the northwest
Chatham Rise was depleted in 2006 and recently most members of
the fishing industry have agreed to refrain from fishing there for a
few years; stocks around the far north and south of New Zealand
appear to have declined substantially in many areas, but are of
unknown status; the two stocks off the west and south coasts of the
South Island were depleted and closed in 2000 and 2007
respectively; however of these the stock on the Challenger Plateau
has been estimated to have recovered and was reopened to
commercial fishing, on a small scale, in 2010 [4]. The final stock,
on the east coast of the North and South Islands (known as the Mid-
East Coast), was expected to be rebuilding after being depleted in
the mid-1990s [5], but a new assessment, in 2011, indicated the
rebuild had not occurred and the stock remains depleted [4].
In New Zealand, various problems with quantitative stock
assessment model assumptions have exacerbated uncertainty in
stock status [2,4,6–8]. One population model assumption being
investigated for Mid-East Coast orange roughy concerns spatial
structure. Previous models have assumed each stock to be a single,
homogenous unit. Spatial structure in the stocks has been
observed, however, with juvenile orange roughy found in greatest
abundance in relatively shallow water (850–900 m), extending into
deeper water as they grow (to about 1500 m), with the largest fish
more frequently found on and around hills (,9 km from the
summit) [9–11]. The year-round association between hills and
large aggregations of orange roughy is well known, with non-
spawning aggregations assumed to occur primarily because the
hills may offer better foraging opportunities [12–13]. Almost all
orange roughy fisheries are spatially distinct, targeting fish
PLoS ONE | www.plosone.org 1 November 2011 | Volume 6 | Issue 11 | e26704
1
aggregations on and around specific underwater features [12], or
on their spawning grounds which sometimes occur over flat
ground. The interaction between spatial stock structure and
fisheries might, in principle, explain some of the problems
encountered in orange roughy stock assessment models. Problems
achieving acceptable stock assessment models for deep-water fishes
are not confined to orange roughy, but have also occurred for
black cardinalfish Epigonus telescopus [14], smooth oreo Pseudocyttus
maculates [15] and black oreo Allocyttus niger [16], species which
have fisheries, like orange roughy, that target aggregations on and
around underwater features such as canyons, ridges, hills and
seamounts (all hereafter referred to as ‘‘hills’’).
Orange roughy have never been tagged to study movements, so
in this study we evaluate indirect biological evidence for spatial
structuring and connectivity, outside of the spawning season, for
two alternative versions of the spatial hypothesis. The first is that,
despite there being aggregations on hills, the net benefits of all
habitats are the same, such that fish feeding, condition, and the
proportion of mature fish that will spawn in a given year is the
same everywhere; in this case, the ecology of orange roughy in
both habitats is similar and mature and reproductively active fish
might be present anywhere. The second is that the ecology of flat
and hill habitats differs such that fish outside of the ‘‘better’’ hill
habitats are in poorer condition, and so spawn less frequently or
not at all; in this case, the spawning biomass would only be fish
from the hills, and any non-spawning (resting) adults [8] would
predominantly be away from the hills. These two different spatial
hypotheses both require ontogenetic habitat shifts and fidelity, as
suggested by previous studies [10,11], but for each the vulnera-
bility of the spawning stock biomass to exploitation could be very
different. It should be possible to distinguish between the two
hypotheses using indirect biological observations, because if there
is no difference in ecology between habitats, then the fish from hill
and non-hill (hereafter referred to as ‘‘flat’’) habitats might show
little difference in diet or feeding activity, and should show no
difference in condition or the proportion of fish with gonads that
had spawned or were developing to spawn that year (hereafter
‘‘reproductive activity’’). In this study we test the ability of different
predictors, including the environmental predictors previously used
to describe patterns in fish length structure [11], to describe the
variability in orange roughy diet, feeding, fish condition as judged
by fish weight, and reproductive activity, using research trawl
survey samples collected outside of the spawning season from the
Mid-East Coast stock. Although the fishery on the Mid-East Coast
stock initially focused on large spawning aggregations on and
around specific hills in the north of the region, since the mid-1990s
the fishery has predominantly targeted non-spawning aggregations
on hills [4]. Whether, and which, environmental predictors are
selected, should indicate which of the competing hypotheses is
more likely to be true. In completing these analyses we also
provide a detailed quantitative description of orange roughy diet,
the only similar previous studies being off the west coast of New
Zealand on Challenger Plateau [17], and off southeast Australia
[18], and discuss the value of different habitats to orange roughy.
Materials and Methods
Ethics
This study was exempt from ethical approval by the NIWA
Animal Ethics Committee.
Diet and feeding statistics
Biological samples of orange roughy were collected from a
stratified random research bottom trawl survey of the Mid-East
Coast during March–April 2010 [19] (Figure 1). The sampling
area covered 17 358 km
2
, depths between 600 and 1500 m, with
strata including flat and sloping continental slope, and hills
(features having vertical elevation $100 m). The 2010 survey,
along with comparable surveys in 1992–94 (Table 1), was timed to
take place before any spawning migrations had started, when
orange roughy distributions were thought to be stable [19].
Spawning for this stock takes place in June and July [5].
Standardised trawl tows were conducted 24 hours a day, with
up to 12 tows completed per day, often across multiple strata. Up
to 20 orange roughy were randomly selected for sampling from all
tows where they were caught. Selected fish were sampled for
standard length (SL, to the nearest mm), total weight (to the
nearest 5 g), sex, gonad weight (to the nearest g), and a
macroscopic assessment of maturity. The stomach and otoliths
were removed for subsequent analysis. At sea, stomachs were
sealed by fixing a cable-tie around the oesophagus, then the
oesophagus was cut in front of the tie, the intestines cut below the
pyloric sphincter, and the stomach removed, labelled, frozen at -
20uC and returned to the laboratory. Fish with obviously
regurgitated or everted stomachs were not sampled for stomachs.
The processing of stomach contents and data analyses followed
previous diet analyses for hoki Macruronus novaezelandiae [20] and
macrourids [21]. Briefly, each stomach was thawed, the wet
weight of stomach and contents recorded, the stomach contents
removed and rinsed with water, and the wet weight of the empty
stomach recorded. Recognisable prey items were then identified.
For each prey category, the individual prey items were counted,
and the wet weight recorded after removal of surface water by
blotting paper.
The weight of the stomach contents as a percentage of total
weight (%S) was calculated as %S = W/(T – F +E) 6100 where
W is the weight of the sorted prey, T is the total fresh fish weight
(including full stomach), F is the weight of the full stomach, and E
is the weight of the empty stomach; this formulation excludes the
weight of fluid and fine material found in the stomach from the
statistic [22]. Variability in %S, and the proportion of stomachs
containing food, were analysed using a series of generalised
additive models (GAMs), as implemented in the mgcv library of
the statistical package R [20,23]. The %S was log transformed and
modelled using an identity function and Gaussian error term; the
proportion of stomachs containing food was modelled using a logit
link and binomial error term. The results of the GAM analyses
were marginal tests, fitting each predictor individually, and a final
GAM which was built followed the guidelines of Wood & Augustin
[23] and Wood [24], with the additional criteria that each final
predictor should be significant (p#0.05) and explain at least 0.1%
of additional deviance. The potential predictors of %S and the
proportion of stomachs containing food included the fish sex;
macroscopic maturity stage, length and weight, and the tow year,
month, time of day; mean depth; difference in depth between the
start and end position, mean water temperature at the surface;
mean water temperature at the bottom; the difference between the
two temperatures; the distance to the summit of the nearest feature
(any known seamount, hill or knoll (having vertical elevation
$100 m) identified in the NIWA SEAMOUNTS database [25]
with a summit depth $600 m and #1500 m; Figure 1); and the
survey stratum (hill or flat; hills were originally defined using the
locations of commercial fishing tows known to be targeting hill
areas [19]). Length, weight, time of day, depth, temperature and
distance from hill predictors, were all treated as continuous and
fitted in the GAM using cubic splines; other predictors were
treated as categorical. Predictor distributions and model residuals
were examined to ensure the model fit was adequate; as a result
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 2 November 2011 | Volume 6 | Issue 11 | e26704
the distance to the summit of the nearest feature, and difference in
depth between the start and end position, were both square
rooted, and depth was log-transformed. Significant and relevant
correlations between predictors are reported in the results. The
deep-sea habitat of the Mid-East Coast is poorly known, such that
more ecologically pertinent habitat predictors (e.g., primary
productivity, presence of biogenic habitat, depth of mesopelagic
layers) were not available at the spatial and temporal scales
required. Predictors for location, such as longitude, latitude, and
region, were not included in the analyses because they are indirect
environmental descriptors; i.e., they have no direct significance to
the fish.
The unidentifiable prey, parasites found in the stomachs, and
prey classified as well digested, were excluded from detailed diet
analyses. The contribution of different prey items to the diet was
determined by the numerical importance (%N), frequency of
occurrence (%F), mass (%W) and percentage index of relative
importance (%IRI) [26,27]. Bootstrap methods, consisting of 1000
replicates of random samples, with replacement, of stomachs from
the original data set, stratified by tow, were used to estimate 95%
confidence intervals around the dietary statistics [28].
To conduct analyses of diet variability, the prey items were
aggregated into taxonomic categories. To assess the adequacy of
the samples, the cumulative diversity (Brillouin index of diversity,
Table 1. Research trawl surveys for the Mid-East Coast
orange roughy.
Survey date Vessel
n
tows
n
orange roughy
measured
Jun 1986* RV James Cook 474
Jun-Jul 1986* FV Otago Galliard 80 1528
Jun-Jul 1987* FV Arrow 76 1270
Oct 1989 FV Will Watch 163 2853
Mar-Apr 1992 RV Tangaroa 165 2311
Mar-Apr 1993 RV Tangaroa 203 3241
Mar-Apr 1994 RV Tangaroa 190 3128
Mar-Apr 2010 RV Tangaroa 154 3104
The number of tows and fish samples available for analyses of orange roughy
condition and reproductive activity. Surveys from 1992 to 2010 were a
standardised time series.
*, samples excluded from analyses of reproductive activity. Research surveys
were completed using either; FV, chartered fishing vessels; or RV, dedicated
research vessels.
doi:10.1371/journal.pone.0026704.t001
Figure 1. Location of the Mid-East Coast orange roughy stock. Locations where stomach samples were collected (circles), and known hill
features (triangles). Dashed line, 600 m isobaths; dotted line, 1500 m isobath. The light grey circle around the hill at the southern border at ORH 2A
has a latitudinal radius of 20 km, and is included only for scale.
doi:10.1371/journal.pone.0026704.g001
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 3 November 2011 | Volume 6 | Issue 11 | e26704
H) of categorised stomach contents was plotted against the
cumulative number of stomachs containing food [29]. The mean
and 95% confidence interval were calculated from 1000 curves
based upon different random orders of the stomachs. The sample
was considered adequate if the mean sample diversity (H) was
$95% of the asymptotic diversity (H
A
), estimated from a fitted
curve of the form H = an/(1 +bn) [22].
Distance-based linear model (DistLM) analysis in PRIMER v6
[30,31] was used to identify which of the potential predictors
explained most of the variability in diet. Prey weight data were first
standardised, which assumed that, within each stomach, weight
was a better descriptor of the diet than occurrence or prey
frequency, and each stomach was an equally good descriptor of
overall diet. The data were then square-root transformed, which
reduced the influence of dominant prey, and a dissimilarity matrix
calculated using Bray-Curtis distances. The potential predictors
were the same as used in the GAM analyses, except year and
month were excluded. Significant and relevant correlations
between predictors are reported in the results. The most significant
predictors were selected using the step-wise selection method, and
the Bayesian Information Criterion (BIC) [31]. The results of the
DistLM analysis were a marginal test, fitting each predictor
individually, and a conditional test, fitting each predictor
conditional on the predictor(s) already in the model. To further
investigate the effects of the predictors identified from the DistLM
analysis, the continuous predictors were binned, with bin
boundaries chosen so that the number of observations in each
bin was approximately equal. The target number of samples in
each bin was sufficiently large to describe .95% of the estimated
diversity of the overall diet. The binned data were averaged (mean
of normalised proportions of prey species weight), square-root
transformed, and then the characteristic prey groups identified
with SIMPER (similarity percentages). SIMPER decomposes the
average Bray-Curtis dissimilarities between all pairs of samples
into percentage contributions from each prey species [30]. The
actual mean percentage weight of the prey groups identified by
SIMPER was then calculated to show the main differences in diet
composition between bins.
Fish condition
Fish condition was investigated by fitting GAMs to fish weight,
thereby assuming that heavier fish of a given length were in better
condition [32]. For the 2010 survey, the weight analysed was
somatic fish weight, defined as total weight minus gonad weight
minus prey weight (weight of the full stomach minus weight of the
empty stomach). To determine whether there were temporal
trends in fish weight, the analysis was then repeated using all
available trawl survey data between 1986 and 2010 (Table 1).
Because gonad and prey weight were not available for all surveys,
the weight used in this analysis was total fish weight.
The GAMs used an identity function and Gaussian error term,
and were fitted in the same way as the analyses of feeding statistics.
The potential predictors were the same as used for the feeding
statistics, except that in the analysis of 2010 somatic fish weight the
time of day (no change in condition was expected over 24 hours)
and macroscopic maturity stage (considered a consequence of fish
condition, not a cause) were both excluded. In the analysis of 1986–
2010 total fish weight, year and month were added, time of day was
retained (as weight included prey weight), and stratum (inconsistent
across surveys) and temperature (not available) were excluded.
Reproductive activity
Orange roughy sampled during trawl surveys outside of the
spawning season (defined as June and July; i.e., the surveys in 1986
and 1987 were excluded; Table 1) that were classified as the
macroscopic maturity stages immature or maturing were grouped
as ‘‘inactive’’ (i.e., not reproductively active), and all other stages
(mature through ripe, running ripe and spent) were classified as
‘‘active’’ [33]. The samples from June and July were excluded to
avoid confounding environmental effects of hills with behaviour
preferences for hills as spawning sites. Samples outside of June and
July would either be post-spawning, or developing to spawn [33].
Similar to the analyses of feeding statistics, a series of GAMs were
used to model the proportion active, using a logit link and
binomial error term. The potential predictors for reproductive
activity were the same as those used for the analysis of 1986–2010
total fish weight.
Results
Feeding statistics
The marginal GAMs on the proportion of stomachs containing
prey indicated fish size, depth, time of day, bottom temperature,
and temperature difference had the strongest influence; with the
habitat predictors, stratum and distance to nearest hill, having no
influence (Table 2). The final GAM had the significant predictors
length (p#0.001) +sex (p#0.05) +bottom temperature (p#0.001),
but explained only 2.8% of the variability. The model suggested a
steady decrease in the proportion of stomachs containing prey with
increasing fish length, that females more often contained prey, and
that the proportion of stomachs containing prey increased with
increasing bottom temperature (Fig. 2). There only correlation
between a final GAM predictor and other predictors was between
bottom temperature and depth (r
2
= 0.84); other correlations were
relatively weak (r
2
#0.32).
The marginal GAMs on stomach fullness (%S) indicated only
surface temperature and temperature difference had a significant
influence (Table 2). The final GAM included only the surface
temperature predictor, explaining 3.8% of the deviance. The
predicted effect was not a simple trend (Fig. 3), and suggested
surface temperature was probably aliasing for something else.
Surface temperature was strongly correlated with temperature
difference (r
2
= 0.90).
Diet
Orange roughy were sampled over a wide spatial area (Fig. 1).
Of the 923 specimens examined, 399 (43%) had empty stomachs.
Of those containing prey, the analyses of stomach contents led to
the identification of 1519 individual prey items in 90 prey groups,
having a total weight of 3298 g (Table S1). The number of prey
items per stomach ranged between 1 and 78, with 95% of
stomachs containing less than 10 prey items, and 50% containing
only a single prey item. Prey remains were all unidentifiable or
well digested in 80 stomachs, leaving 444 for detailed analyses of
diet. The 444 specimens were sampled from a median depth of
956 m (range 671–1386 m), and had a median length of 29.9 cm
SL (range 12.1–42.4 cm SL). New types of prey continued to be
identified with increasing sample size (Fig. 4A), but the diversity of
prey categories reached 95% of the estimated asymptote after 95
stomachs (Fig. 4B), indicating that the sample was large enough to
describe the diversity of the diet using the assumed prey
categorisation.
The diet of orange roughy was characterised by bathypelagic or
mesopelagic bony fishes, crustaceans, and cephalopods (Table S1).
Fishes were the most important prey, accounting for 66.8% of the
total prey weight. The most important fish prey were lanternfishes
(Myctophids; accounting for 15.7% of total prey weight), of which
there were at least nine species, followed by small mouth fishes
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 4 November 2011 | Volume 6 | Issue 11 | e26704
(Nansenia spp.; 9.1%), bigscale fishes (Melamphaidae, 6.0%),
rattails (Macrourids; 5.2%), and various small fishes such as
waryfish (Scopelosaurus sp.), daggertooths (Paralepididae), and
viperfish (Chauliodus sloani). Crustaceans were by far the most
common prey, but being relatively small contributed only 21% of
the total prey weight. The most important crustacean prey were
mysids, shrimps and prawns, predominantly Pasiphaea spp. (6.4%
of total prey weight), Lophogastridae (3.3%). Sergestes arcticus
(1.7%), and Boreomysinae (1.4%). Pasiphaea aff. tarda were less
frequent than P. aff. sivado, but were relatively large and
contributed more prey weight. Cephalopods accounted for 11%
of total prey weight, and were predominantly bathypelagic squid
such as Cranchiidae, including the transparent Teuthowenia
pellucida, and Onychoteuthidae. Minor prey items included salps
and an echinoderm, although the latter seems likely to have been
incidental ingestion.
The DistLM analysis indicated significant relationships between
diet and several of the predictors (Table 2), with the sequential
model having the predictors fish length +depth +surface
temperature, together explaining 8.3% of the deviance. There
was a significant correlation between depth and bottom temper-
ature (r
2
= 0.80, p#0.001), and the deviance explained by a
Figure 2. Proportion of orange roughy stomachs containing prey. P(containing prey), generalised additive model predictions (points or solid
lines) with 1 SE (dotted lines) for A, orange roughy length; B, sex; and C, bottom temperature, made with all other predictors set to their median
(fixed) values. The rug on the x-axis for length and bottom temperature indicates the data points (n= 892), for sex nis shown above x-axis.
doi:10.1371/journal.pone.0026704.g002
Table 2. Percentage deviance explained by predictors in GAM and DistLM analyses.
Predictor
P(containing
prey) %S
Diet
composition
Fish somatic
weight 2010
Fish total weight
1986–2010
Reproductive
activity
Length 1.3** 0.3
NS
5.6*** 96.7*** 95.8*** 38.8***
Weight 1.3** 0.7
NS
4.9*** – – –
Sex 0.2
NS
0.01
NS
0.4
NS
5.9*** 3.9*** 19.8***
Gonad stage 0.9* 0.01
NS
4.5*** 59.9*** 61.6*** –
Depth 2.3*** 0.4
NS
2.2*** 7.9*** 3.5*** 0.7***
Depth difference 1.5* 1.9
NS
0.5* 2.1
NS
2.2*** 1.6***
Surface temperature 0.7
NS
3.8* 1.9*** 9.3*** – –
Bottom temperature 1.5*** 0.04
NS
1.6*** 2.0
NS
––
Temperature difference 2.1** 2.1* 2.0*** 9.6*** – –
Stratum 0.1
NS
0.01
NS
0.5** 0.01
NS
––
Distance to nearest hill 0.1
NS
0.1
NS
0.5* 13.0*** 12.7*** 5.0***
Time of day 2.2** 0.9
NS
0.3
NS
– 0.6*** 0.1*
Month – – – – 5.3*** 5.4***
Year – – – – 5.2*** 5.4***
Percentage of deviance explained in marginal tests using Generalised Additive Models for the proportion of orange roughy containing prey, %S, fish weight during the
survey in 2010 and surveys between 1986 and 2010, and for diet composition using the DistLM analysis. Approximate significance of predictors: NS, .0.05;
*#0.05;
**#0.01;
***#0.001.
doi:10.1371/journal.pone.0026704.t002
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 5 November 2011 | Volume 6 | Issue 11 | e26704
sequential model including length and depth (7.12%) was only
marginally better than one using length and bottom temperature
(7.11%). The next strongest correlation between final model
predictors and other predictors was a weak correlation between
bottom temperature and surface temperature (r
2
= 0.11).
The diet of small orange roughy was characterised by
Boreomysinae, P. aff. sivado, and S. arcticus (Table 3). As orange
roughy got larger, the diet featured less Boreomysinae and
Pasiphaea spp., and was characterised more by S. arcticus,Sergia
potens, Oplophoridae, the Onychoteuthidae squids, and lantern-
fishes Lampanyctodes spp. and Lampanyctus spp.
In relatively shallow water, the diet of orange roughy was
characterised by Boreomysinae, P. aff. sivado,S. arcticus,and
Lampanyctodes spp. (Table 4). As depth increased, the crustacean diet
featured less Boreomysinae and P. aff sivado, and more amphipods, P.
aff tarda,S. potens, Lophogastridae, and Oplophoridae, and within the
fish diet, Lampanyctodes spp. were replaced by Lampanyctus spp.,
Macrouridae and Melamphidae. There was only a weak correlation
between orange roughy length and depth (r
2
=0.28).
The diet in cooler surface water was characterised by
Boreomysinae, Lampanyctus spp., P. aff. sivado, and Petalophthal-
midae (Table 5). In warmer water, the diet featured less
Boreomysinae and more S. potens.Lampanyctodes spp. replaced
Figure 3. Orange roughy stomach fullness. Stomach fullness (%S)
generalised additive model prediction (solid line) with 1 SE (dotted
lines) for surface temperature. The rug on the x-axis indicates the data
points (n= 507).
doi:10.1371/journal.pone.0026704.g003
Figure 4. Orange roughy number of prey types and prey diversity with increasing number of stomachs sampled. Panel A, mean
cumulative number of prey types identified. Panel B, mean cumulative diversity of prey categories (measured using the Brillouin index of diversity, H).
Broken lines indicate the 95% CIs. Dotted line in B is a fitted curve from which asymptotic diversity was estimated. Stomachs containing all
unidentifiable or well-digested prey were excluded.
doi:10.1371/journal.pone.0026704.g004
Table 3. Orange roughy diet by fish length group.
12.1–25.3 25.4–29.7 29.8–33.3 33.4–42.4
n106 112 111 115
Amphipoda 5.6 3.7 6.3
a
1.6
Boreomysinae 36.6
c
18.9
c
2.0 0.6
Lampanyctodes spp. 2.8 10.0
a
5.2
a
7.6
a
Lampanyctus spp. 0.7 3.4 8.3
b
7.8
a
Lophogastridae 0.0 2.0 7.9
a
6.8
a
Onychoteuthidae 0.0 0.0 2.7 4.2
a
Oplophoridae 1.2 1.0 3.9
a
10.0
b
Pasiphaea aff. sivado 10.5
a
12.3
b
3.1 2.8
Pasiphaea aff. tarda 7.4 8.0
a
6.5
a
3.0
Petalophthalmidae 5.0 4.7 3.7
a
0.9
Salpida 3.2 2.8 3.5
a
0.9
Sergestes arcticus 12.8
a
11.8
b
13.1
c
15.1
c
Sergia potens 0.8 3.4 7.2
a
7.6
a
Mean of standardised percent prey weight within the fish length groups (SL in
cm), for the prey types together contributing at least 90% of the SIMPER within
group similarity for one or more groups. SIMPER percentage contribution to
within group similarity:
a
3–10%;
b
10–30%;
c
.30%;
no superscript, not identified by SIMPER as characteristic for that group;
n, sample size.
doi:10.1371/journal.pone.0026704.t003
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 6 November 2011 | Volume 6 | Issue 11 | e26704
Lampanyctus spp., and P. aff. tarda replaced P. aff. sivado.
Amphipoda and Lophogastridae were characteristic of intermedi-
ate surface temperatures.
Fish condition
The marginal GAMs on fish somatic weight in 2010 indicated
length, gonad stage, and distance to the nearest hill had the
strongest influence (Table 2). The final GAM had the predictors
fish length +gonad stage +distance from the nearest hill, and
explained 96.9% of the deviance. The predicted effects showed
somatic weight increased with increasing orange roughy length,
increased by a small amount (about 6%) with maturity, and varied
(by about 4%) with distance from the nearest hill, declining within
about 20 km, and further than about 80 km from the nearest hill
(Fig. 5).
The marginal GAMs on total weight for 1986–2010 found all
predictors were significant, but fish length, gonad stage, distance
from the nearest hill, and year had the greatest influence (Table 2).
The final GAM had the predictors fish length +gonad stage +
year, and explained 96.1% of the deviance. The predicted effects
showed an increase in weight with increasing fish length, and an
increase in weight (about 10%) when mature and ripe, consistent
with enlarged gonads during these stages, with lowest weight when
immature or spent (Fig. 6). The predicted year effect was small
(about 3% variability in weight) and suggested inter-annual
variability, with no trend (Fig. 6).
Reproductive activity
The marginal GAMs on reproductive activity were significant
for all predictors (Table 2). The final GAM had the predictors fish
length +month +distance from the nearest hill, and explained
46.6% of the deviance. Reproductive activity increased with fish
length, was higher in March and April, and was highest at
distances of about 25–50 km from the nearest hill (Fig. 7).
Discussion
Known spatial structuring in the relative abundance of orange
roughy, with greater catches on hills than flat habitats, was not
easily explained by their ecology, at least for the parameters
included in this study. Despite obvious differences in the physical
attributes of the hill and flat habitats, and the fact that more fishing
for orange roughy had occurred on hills, the ecology of the two
habitats was not notably different for orange roughy in terms of
amount and type of prey they consumed. Further, orange roughy
on hills were not more reproductively active or heavier, and
therefore not in better condition than fish caught on flat habitat.
These findings apply to the period outside of spawning, and for the
duration of our sample collection. Large-scale redistribution of
adult fish is known to take place during spawning (as spawning
migrations), and because the trawl surveys were temporal
‘snapshots’, it is unknown whether these results would be
representative of the entire non-spawning season, or for other
years.
We found environmental predictors could explain significant
variability in orange roughy diet, feeding statistics, somatic
condition and reproductive activity. The variability explained by
the available environmental predictors was small however,
suggesting that the overall influence of environment on orange
roughy biology was small, and might reasonably be ignored when
modelling population dynamics. In other words, we believe the
spatial structure was not pronounced enough to justify the
additional model complexity needed to allow for it, i.e., additional
estimable parameters, with the resulting additional uncertainties
being propagated into model predictions. As a result, stock
assessment models might best assume our first hypothesis; that all
mature fish, regardless of their location, can equally contribute to
the spawning stock in any given year.
Table 4. Orange roughy diet by depth group.
671–870 873–951 961–1080 1081–1386
n108 114 110 112
Amphipoda 2.6 3.8 4.0 6.7
a
Boreomysinae 18.6
c
18.2
c
10.4
b
9.4
b
Lampanyctodes spp. 19.8
b
4.5 0.9 1.0
Lampanyctus spp. 1.6 6.8
b
5.7
a
6.3
a
Lophogastridae 1.6 3.2 7.2
a
4.9
a
Macrouridae 0.9 0.9 0.1 3.4
a
Melamphaidae 0.0 0.9 2.6 5.4
a
Oplophoridae 3.9 0.9 4.8
a
7.0
b
Pasiphaea aff. sivado 16.9
b
5.8
b
3.4 2.5
a
Pasiphaea aff. tarda 7.1 10.1
b
4.1
a
3.4
a
Petalophthalmidae 2.9 5.2
b
3.6 2.4
Salpida 2.5 0.8 5.2
a
1.8
Sergestes arcticus 10.1
b
17.5
c
18.2
c
6.9
b
Sergia potens 1.7 3.6 8.1
a
5.9
a
Mean of standardised percent prey weight within the depth (m) groups, for the
prey types together contributing at least 90% of the SIMPER within group
similarity for one or more groups. SIMPER percentage contribution to within
group similarity:
a
3–10%;
b
10–30%;
c
.30%;
no superscript, not identified by SIMPER as characteristic for that group;
n, sample size.
doi:10.1371/journal.pone.0026704.t004
Table 5. Orange roughy diet by surface temperature group.
13.1–15.2 15.3–16.3 16.6–18.9 19.0–20.2
n103 111 113 117
Amphipoda 4.5 5.9
a
3.7 3.1
Boreomysinae 17.8
c
14.8
c
17.4
c
7.2
a
Lampanyctodes spp. 0.9 0.9 11.8
b
11.4
b
Lampanyctus spp. 8.7
a
6.4
a
4.2 1.7
Lophogastridae 2.9 3.5 8.6
a
1.8
Pasiphaea aff. sivado 14.3
b
11.9
b
2.1 1.0
Pasiphaea aff. tarda 6.9
a
7.0 7.3
a
7.9
a
Petalophthalmidae 6.7
a
7.0
a
0.0 0.8
Sergestes arcticus 11.0
b
8.9
b
13.5
b
19.0
c
Sergia potens 3.0 1.8 7.0
a
7.3
a
Mean of standardised percent prey weight within the surface temperature (uC)
groups, for the prey types together contributing at least 90% of the SIMPER
within group similarity for one or more groups. SIMPER percentage
contribution to within group similarity:
a
3–10%;
b
10–30%;
c
.30%;
no superscript, not identified by SIMPER as characteristic for that group;
n, sample size.
doi:10.1371/journal.pone.0026704.t005
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 7 November 2011 | Volume 6 | Issue 11 | e26704
The relatively small influence of environmental predictors may
have been because we did not include the most relevant predictors.
Alternatively there was genuinely little variability associated with
environmental variability. The use of diet and feeding statistics as
indicators of habitat variability is the least convincing, primarily
because of the sampling of only short time periods [34]. The Mid-
East Coast is also a region of complex topography, such that the
difference in environmental conditions between hills and flats
might be relatively low compared to, for example, a region where
a single seamount rises out of a broad surrounding area of flat
seabed. While our analyses did not identify strong influences of the
environment, they nevertheless give insights into orange roughy
biology and hypotheses of spatial structure.
The final analyses for diet and feeding statistics did not select
environmental predictors directly associated with the hill versus
flat habitat, but fish condition and reproductive activity were
estimated to be higher away from the hills. The hills in the analyses
all had radii of less than 10 km, so the increase in somatic
condition and reproductive activity was outside of the direct
influence of the hill. Outside of the spawning season, the
reproductively active fish on the flats might not be available to a
fishery targeting hills. When most of the fishery catch is taken
outside of the spawning season, as is now the case for the Mid-East
Coast stock, this interaction of fish and fishery distribution might
allow a spatially unavailable or ‘‘cryptic’’ spawning stock biomass
to occur.
Figure 5. Orange roughy somatic weight from 2010. Generalised additive model predictions (points and solid line) with 1 SE (dotted lines) for
A, standard length; B, macroscopic gonad stage; C, distance from the nearest hill. The rug on the x-axis for length and distance from hill indicates the
data points (n= 830), for gonad stage nis shown above x-axis. Immature indicates immature or resting.
doi:10.1371/journal.pone.0026704.g005
Figure 6. Orange roughy total weight from 1986–2010. Generalised additive model predictions (points and solid line) with 1 SE (dotted lines)
for A, standard length; B, macroscopic gonad stage; and C, year. The rug on the x-axis for length indicates the data points (n= 17 521), for gonad
stage and year nis shown above x-axis. Imm., immature or resting; Mat., maturing; Ripe, ripe and running; M., mature; Sp., spent.
doi:10.1371/journal.pone.0026704.g006
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 8 November 2011 | Volume 6 | Issue 11 | e26704
Aggregations of orange roughy on hills have been the focus for
orange roughy commercial fisheries worldwide, even though
orange roughy can be ubiquitous in low densities on the
continental slope [57]. The predominance of larger orange roughy
on hills [9-11] indicates that stocks are not homogenous, and
might be caused by intra-specific competition for the best habitat.
Previous studies have considered the likely value of hill habitats to
orange roughy, and concluded hills are more favourable feeding
grounds, most likely because of enhanced horizontal flux of
mesopelagic prey and extended contact with the mesopelagic
layers, and because the seabed is closer and more rugged and so
may provide refuges in which to rest or escape from predators
[12,13,58,59]. Predictors describing flat versus hill habitats were
not selected in our analysis of diet variability, supporting the
horizontal prey flux hypothesis rather than hypotheses which
presuppose a unique prey fauna available on, or trapped above,
hills [58,60]. Further spatial structure within hill or flat habitats, in
response to environmental heterogeneity, does seem quite possible.
However, examining this additional spatial structure would
require observational data at a scale that does not currently exist
for orange roughy.
Orange roughy catch rates in the Mid-East Coast trawl surveys
were at least three times higher on hills than flats [19], consistent
with hills being a preferred habitat, but our analyses indicated hill
fish were not superior. This is probably not a bias in interpretation
brought about by orange roughy being easier to catch on hills, as if
anything we might expect trawl efficiency to be lower on hills
because of the rougher ground. However, trawl independent data,
e.g., acoustic surveys, to confirm the higher biomass on hills are
not available; acoustic surveys to date have been focused on
orange roughy spawning aggregations [5,8]. At initial biomass
levels, the high fish density on hills might result in little net benefit
to individuals, but the Mid-East Coast stock has been fished down
and catch rates on hills have declined [5,35] indicating a reduction
in local density, and as a result for an individual orange roughy the
hill habitat should be better, due to reduced competition for food
and resources [12–13]. We consider that there are two main
hypotheses for hill fish not being superior. First, fish in relatively
poor condition may visit hills in order to feed and regain
condition, and once this is achieved, they leave. This would imply
there are risks of being on hills that outweigh improved feeding
opportunities once the fish have gained sufficient condition.
Certainly, some hills do attract aggregations of deep-water sharks
that are known to predate orange roughy, although it is not clear if
orange roughy are eaten live, in the net, or scavenged [36], and
fishers report that the presence of orange roughy aggregations on
some hills can be intermittent. This ‘‘transient fish’’ hypothesis
might be tested by examining temporal changes in the density of
orange roughy on hills in relation to fish condition. The second
hypothesis is that commercial fishing has disturbed feeding
aggregations and/or caused habitat damage, making fished hills
a less favourable habitat. Benthic habitat damage by trawling can
be substantial, and has been well documented [37,38]. However,
some hill areas have continued to support large orange roughy
fisheries despite extensive trawling, suggesting benthic habitat
damage may not be that influential [38]. For example the Andes
hill complex off eastern New Zealand has remained the centre of
the Chatham Rise orange roughy non-spawning fishery despite
just over 8800 targeted trawl tows completed over 18 years, in an
area less than 18 km across [39]. Disturbance of feeding behaviour
by fishing might be substantial however, and fishers often cite
recent disturbance as a reason for low catch rates, and try to avoid
areas recently fished as a result. This ‘‘fishing disturbance’’
hypothesis could be investigated by examining feeding success
(e.g., %S) in relation to time since disturbance, or fish condition in
relation to accumulated disturbance.
The diet of orange roughy on the Mid-East Coast was similar to
that observed elsewhere, consisting of bentho- and mesopelagic
crustaceans, fishes and squid, with crustaceans predominating in
the diet of small orange roughy, shifting to fish and some squid as
orange roughy got larger [17,18]. We analysed diet at a more
detailed taxonomic level than previous studies, which typically
analysed diet at a prey family, order, or class level, and we found
some changes in diet with depth and surface water temperature at
the prey genus and species level. For example, within the
Myctophidae we found mostly Lampanyctus spp. in the diet in
warm shallow water, and Lampanyctodes spp. in deep cold water, and
within the natant decapods we found a shift from P. aff. sivado in
shallow water to P. aff. tarda in deep water. Details of the
distribution of the prey species are lacking, so it is unknown
Figure 7. Proportion of orange roughy classified as reproductively active. Using data from 1989–2010, generalised additive model
predictions (points and solid line) with 1 SE (dotted lines) for A, fish length; B, month; and C, distance from the nearest hill. The rug on the x-axis for
length and distance from hill indicates the data points (n= 14 647), for month nis shown above the x-axis.
doi:10.1371/journal.pone.0026704.g007
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 9 November 2011 | Volume 6 | Issue 11 | e26704
whether these changes in diet are meaningful, but it seems
reasonable to assume that they may reflect prey availability.
Orange roughy appear to be opportunistic predators, within the
constraints of their morphology and benthopelagic habitat.
The feeding statistics indicated that smaller sized and female
orange roughy in warmer bottom water were most likely to
contain food. The decrease in the proportion of orange roughy
stomachs containing prey with ontogeny has similarly been found
in the deep-sea hoki [20], and may be a ubiquitous pattern if
smaller fish feed more frequently because of a higher metabolic
rate [40]. The increase in occurrence of prey in warmer water
could be related to an increased metabolic rate [41]. Orange
roughy extend into deeper and cooler water as they grow [10], and
as the occurrence of prey decreased with increasing depth, it
appears that larger fish move into deeper water not because of
better feeding opportunities, but perhaps because of reduced
metabolic costs in the cooler water, or evolutionary benefits such
as reduced intraspecific competition or natural mortality [20,42–
44]. Sexual dimorphism in orange roughy is not especially
pronounced, with females typically growing only a little faster
and longer than males [45], therefore the more frequent
occurrence of prey in females might be related to the higher
energetic requirement of egg production. While the chosen
significant predictors did not explain much of the variability in
the proportion of orange roughy stomachs containing prey, the
predicted effects did seem reasonable. The predicted effect of
surface temperature on %S was not reasonable, however, and
suggested surface temperature was not the true cause, but was
aliasing for some other, possibly spatial, effect.
Although we did not detect a significant circadian pattern in
feeding, in accordance with Rosecchi et al. [17], and with there
being no significant diel patterns in demersal trawl catch rates
[46], Bulman & Koslow [18] found stomach fullness in adult
orange roughy peaked during the night. As the main orange
roughy prey are mesopelagic and thought to migrate towards
surface waters at night, including Lampanyctodes spp.,Lampanyctus
spp.,S. arcticus, and Pasiphaea spp. [47,48], night time foraging adult
orange roughy would presumably also have to make vertical
migrations. In order to explain their observations, while assuming
orange roughy had a demersal habit, Bulman & Koslow [18]
suggested the orange roughy prey, including S. arcticus and
Lampanyctus spp., migrated up the slope from deeper water and
through the orange roughy depths on their way to and from the
surface water at night. However, in principle, dispersed orange
roughy might forage both night and day, extending at night some
distance into midwater, where they would not be obvious in
acoustic surveys because of their low acoustic target strength [49],
and where they might not be caught in midwater trawls [10]
because of a strong dive-response to disturbance [50]. In order to
explain the lack of diel patterns in demersal trawl catch rates, any
vertical excursions would probably have to be moderate (perhaps
,300 m), and there would have to be a pronounced dive-response
to the trawl warps, such that orange roughy were close to the
seabed when a demersal trawl net reached them. The observed
differences in orange roughy diet with depth could therefore reflect
changes in demersal prey distribution and, if we assume that
orange roughy foraged within a moderate distance above the
seabed, also a pelagic depth stratification of mesopelagic prey.
Juvenile orange roughy could be different however, and not make
vertical migrations, as their main mysid prey do not migrate
(Boreomysis rostrata has a constant mean depth of 600 m [51]), and
Bulman & Koslow [18] found more fresh food in their stomachs
towards the end of the day, suggesting juvenile orange roughy fed
most actively during the day when mesopelagic layers were closer
to the seabed; this could suggest an ontogenetic difference in
orange roughy foraging behaviour.
To analyse fish condition we could have looked at deviations of
individual fish from the conventional allometric model of the form
Weight = a 6Length
b
[52] but preliminary investigations with
this model, for the entire data set and by year, found a good fit to
most of the data but a consistent positive deviation from the model
fit for small (about ,20 cm SL) orange roughy. This positive
deviation from the conventional growth model could have been
interpreted as persistent good condition in small fish. However, it
could also simply have been a morphological feature of orange
roughy growth. By using the GAM, which allowed more flexibility
in fitting the length effect, our analysis removed any potential
variability in condition associated with ontogeny, other than that
associated with maturity. The analysis of somatic weight indicated
an increase in condition associated with the onset of maturity,
which in other species has been attributed to fish in better
condition being more able to achieve maturity [53-55].
Fish maturity is typically modelled as a function only of age or
size, but the deviance in reproductive activity explained by fish
size, month, and distance from hill in this study (46.6%) suggested
it is more complex than that for orange roughy, perhaps because
of a proportion of mature fish do not spawn in a given year [8,56],
or perhaps true reproductive activity in orange roughy is difficult
to determine. The prediction of higher reproductive activity in
March and April, preceding spawning in June and July, is difficult
to explain, as it would have to be attributed to the gonad
development of maturing fish, which would require the samples to
be dominated by first time spawners, or there would have to be
substantial gonad atresia in the two months before spawning in
June and July. At present, both seem unlikely explanations.
Because sample season and year were to some extent confounded,
the predicted seasonal effect might have been aliasing for a year
effect, which could be a result of inter-annual variability in the
proportion not spawning. Alternatively, it might be attributed to
confusion between macroscopically spent (active) and immature or
maturing (inactive) fish. Similar to somatic condition, reproductive
activity increased away from the hills, although then decreased
after about 50 km from the summit as opposed to after about 70
km in somatic condition. Orange roughy away from hills outside of
the spawning season were in better condition and also more
reproductively active, tending to support the hypothesis that fish
condition is linked to reproductive activity.
Although hills habitat might have little benefit to individual fish,
the influence of available hill habitat on population size through
supporting local high fish densities could still be substantial, and
areas with more hills may well support a larger stock. Because
observations from hills may not be representative of the whole
stock, over short time periods (perhaps 3–10 years), orange roughy
stock assessments informed by hill biomass trends and demo-
graphic data may be misleading about stock size and status. Over
longer time periods, the hills may act as fish aggregating devices
and maintain catch rates despite a continuing decline in stock
biomass, and fishing on hills could even have a depensatory effect
by disrupting feeding. To convincingly model these effects and
estimate stock size and status in the absence of absolute biomass
estimates, stock assessment models need to allow for spatial
structure and movement, and will require spatially stratified catch
and biomass information.
Supporting Information
Table S1 Orange roughy diet composition. Bold text lines
show the point estimates, and 95% confidence intervals estimated
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 10 November 2011 | Volume 6 | Issue 11 | e26704
by bootstrap resampling, of the percentage frequency of
occurrence (%F), percentage weight (%W), percentage number
(%N), and percentage Index of Relative Importance (%IRI), for
prey grouped at the taxonomic levels used in the multivariate
analyses (n = 444). Under each prey group, the normal text lines
show the point estimates of the dietary statistics when calculated
for all prey types (i.e., at full resolution), with the prey types that
could not be allocated to one of the prey groups (so excluded from
multivariate analyses) listed at the bottom of the table (n = 524).
(DOC)
Acknowledgments
Thanks to Malcolm Clark, Ian Doonan, Darren Stevens, and staff who
helped collected samples on the research voyages (all NIWA), and Pamela
Mace (New Zealand Ministry of Fisheries) and one anonymous reviewer
for thoughtful and constructive comments on the draft manuscript.
Author Contributions
Conceived and designed the experiments: MD. Performed the experi-
ments: JF MD. Analyzed the data: MD. Contributed reagents/materials/
analysis tools: JF. Wrote the paper: MD.
References
1. Branch TA (2001) A review of orange roughy Hoplostethus atlanticus fisheries,
estimation methods, biology and stock structure. In AIL Payne, SC Pillar, RJM
Crawford (eds.). A decade of Namibian fisheries science. S Afr J Mar Sci 23:
181–203.
2. Sissenwine MP, Mace PM (2007) Can deep water fisheries be managed
sustainably?In: Report and documentation of the expert consultation on deep-
sea fisheries in the high seas. FAO Fish Rep 838: 61–111. Rome: FAO.
3. Clark MR (2009) Deep-sea seamount fisheries: a review of global status and
future prospects. Lat Am J Aquat Res 37: 501–512.
4. Ministry of Fisheries (2011) Report from the Fisheries Assessment Plenary, May
2010: stock assessments and yield estimates. Available: http://www.fish.govt.nz
via the Internet. Accessed 2011 Sep 29.
5. Dunn MR (2005) CPUE analysis and assessment of the Mid-East Coast orange
roughy stock (ORH 2A South, 2B, 3A) to the end of the 2002-03 fishing year. N
Z Fish Assess Rep 2005/18. Wellington: Ministry of Fisheries.
6. Francis RICC (2006) Some recent problems in New Zealand orange roughy
assessments. N Z Fish Assess Rep 2006/43. Wellington: Ministry of Fisheries.
7. Dunn MR, Ardron JA, Gianni M, Menezes G (2011) Sustainability of deep-sea
fisheries: orange roughy. In: Can ecosystem-based fishing be sustained? Report
of a workshop held 31 August-3 September 2010. USA: University of Maine,
Available: http://www.bloomassociation.org/bloom/download/bloomreport
_lowfinal.pdf via the Internet. Accessed 2011 Sep 29.
8. Dunn MR, Anderson OF, Doonan IJ (2008) Evaluation of stock status for
orange roughy on the east and south Chatham Rise for 2008. N Z Fish Assess
Rep 2008/65. Wellington: Ministry of Fisheries.
9. Shephard S, Rogan E (2006) Seasonal distribution of orange roughy (Hoplostethus
atlanticus) on the Porcupine Bank west of Ireland. Fish Res 77: 17–23.
10. Dunn MR, Rickard GJ, Sutton PJH, Doonan IJ (2009) Nursery ground s of the
orange roughy around New Zealand. ICES J Mar Sci 66: 871–885.
11. Dunn MR, Devine JA (2010) An holistic approach to determining stock
structure of orange roughy on the Chatham Rise. N Z Fish Assess Rep 2010/17.
Wellington: Ministry of Fisheries.
12. Morato T, Clark MR (2007) Seamount fishes: ecology and life histories. In:
Pitcher TJ, Morato T, Hart PJB, Clark MR, Haggan N, Santos RS, eds.
Seamounts: Ecology, Conservation and Management. Fish and Aquatic
Resources Series. Oxford: Blackwell. pp 170–188.
13. Rowden AA, Dower JF, Schlacher TA, Consalvey M, Clark MR (2010)
Paradigms in seamount ecology: fact, fiction, and future. Mar Ecol 31(Suppl 1):
226–239.
14. Dunn MR (2009) Review and stock assessment of black cardinalfish (Epigonus
telescopus) on the east coast North Island, New Zealand. N Z Fish Assess Rep
2009/39. Wellington: Ministry of Fisheries.
15. Doonan IJ, Coburn RP, McMillan PJ (2009) Assessment of OEO 3A smooth
oreo for 2008–09. N Z Fish Assess Rep 2009/47. Wellington: Ministry of
Fisheries.
16. Doonan IJ, Coburn RP, McMillan PJ (2009) Assessment of OEO 3A black oreo
for 2006–07. N Z Fish Assess Rep 2009/12. Wellington: Ministry of Fisheries.
17. Rosecchi E, Tracey DM, Webber WR (1988) Diet of orange roughy, Hoplostethus
atlanticus (Pisces: Trachichthyidae) on the Challenger Plateau, New Zealand. Mar
Biol 99: 293–306.
18. Bulman CM, Koslow JA (1992) Diet and food consumption of a deep-sea fish,
orange roughy Hoplostethus atlanticus (Pisces: Trachichthyidae), off southeastern
Australia. Mar Ecol Prog Ser 82: 115–129.
19. Doonan IJ, Dunn MR (2011) Trawl survey of Mid-East Coast orange roughy,
March-April 2010. N Z Fish Assess Rep 2011/20. Wellington: Ministry of
Fisheries.
20. Connell AM, Dunn MR, Forman J (201 0) Diet and dietary variation in the hoki
Macruronus novaezelandiae. N Z J Mar Fresh Res 44: 289–308.
21. Stevens D, Dunn MR (2011) Different food preferences in four sympatric deep-
sea Macrourid fishes. Mar Biol 158: 59–72.
22. Dunn MR (2009) Feeding habits of the ommastrephid squid Nototodarus sloanii on
the Chatham Rise, New Zealand. N Z J Mar Freshw Res 43: 1103–1113.
23. Wood SN, Augustin NH (2002) GAMs with integrated model selection using
penalized splines and applications to environmental modelling. Ecol Model 157:
157–177.
24. Wood SN (2001) mgcv: GAMs and generalized ridge regression for R. R News
1: 20–25.
25. Rowden AA, Oliver M, Clark MR, Mackay K (2008) New Zealand’s
‘‘SEAMOUNT’’ database: recent updates and its potential use for ecological
risk assessment. N Z Aquat Environ Biodivers Rep 27. Wellington: Ministry of
Fisheries.
26. Pinkas L, Oliphant MS, Iverson LK (1971) Food habits of albacore, bluefin tuna,
and bonito in California waters. Fish Bull Calif Dept Fish Game 152.
27. Corte´s E (1997) A critical review of methods of studying fish feeding based on
analysis of stomach contents: application to elasmobranch fishes. Can J Fish
Aquat Sci 54: 726–738.
28. Tirasin EM, Jørgensen T (1999) An evaluation of the precision of diet
description. Mar Ecol Prog Ser 182: 243–252.
29. Koen Alonso M, Crespo AE, Garcı
´a AN, Pedraza NS, Mariotti AP, et al. (2002)
Fishery and ontogenetic driven changes in the diet of the spiny dogfish, Squalus
acanthias, in Patagonian waters, Argentina. Environ Biol Fish 63: 193–202.
30. Clarke KR, Gorley RN (2006) PRIMER v6: User manual/tutorial. Plymouth:
PRIMER-E.
31. Anderson MJ, Gorley RN, Clarke KR (2008) PERMANOVA+for PRIMER:
guide to software and statistical methods. Plymouth: PRIMER-E.
32. Froese R (2006) Cube law, condition factor and weigh t-length relationships:
history, meta-analysis and recommendations. J Appl Ichth 22: 241–253.
33. Pankhurst NW, McMillan PJ, Tracey DM (1987) Seasonal reproductive cycles in
three commercially exploited fishes from the slope waters off New Zealand. J Fish
Biol 30: 193–212.
34. Pinnegar JK, Trenkel VM, Tidd AN, Dawson WA, Du buit MH (2003) Does
diet in Celtic Sea fishes reflect prey availability? J Fish Biol 63, Suppl 1: 197–212.
35. Dunn MR, Anderson OF (2008) Analysis of catch per unit effort and length
frequency data for the Mid-East Coast (ORH 2A South, ORH 2B & OR H 3A)
orange roughy stock. N Z Fish Assess Rep 2008/23. Wellington: Ministry of
Fisheries.
36. Hallett CS, Daley RK (2011) Feeding ecolo gy of the southern lanternshark
(Etmopterus baxteri) and the brown lanternshark (E. unicolor) off southeastern
Australia. ICES J Mar Sci 68: 157–165.
37. Althaus F, Williams A, Schlacher TA, Kloser RK, Green MA, et al. (2009)
Impacts of bottom trawling on deep-coral ecosystems of seamounts are long-
lasting. Mar Ecol Prog Ser 397: 279–294.
38. Clark MR, Rowden AA (2009) Effect of deepwater trawling on the macro-
invertebrate assemblages of seamounts on the Chatham Rise, New Zealand.
Deep Sea Res I 56: 1540–1554.
39. Anderson OF, Dunn MR (2008) Descriptive analysis of catch and effort data
from New Zealand orange roughy fisheries in ORH 1, 2A, 2B, 3A, 3B, and 7B
to the end of the 2006-07 fishing year. N Z Fish Assess Rep 2008/58.
Wellington: Ministry of Fisheries.
40. Post JR, Lee JA (1996) Metabolic ontogeny of teleost fishes. Can J Fish Aquat Sci
53: 910–923.
41. Clarke A, Johnston NM (1999) Scaling of metabolic rate with body mass and
temperature in teleost fish. J Anim Ecol 68: 893–905.
42. Werner EE, Gilliam JF (1984) The ontogenetic niche and species interactions in
size-structured populations. Ann Rev Ecol Syst 15: 393–425.
43. Macpherson E, Duarte CM (1991) Bathymetric trends in demersal fish size: is
there a general relationship? Mar Ecol Prog Ser 72: 103–112.
44. Griffiths D, Harrod C (2007) Natural mortality, growth parameters, and
environmental temperature in fishes revisited. Can J Fish Aquat Sci 64:
249–255.
45. Tracey DM, Horn PL (1999) Background and review of ageing orange roughy
(Hoplostethus atlanticus, Trachichthyidae) from New Zealand and elsewhere.
N Z J Mar Freshw Res 33: 67–86.
46. Mormede S (2009) CPUE analysis of areas of the Chatham Rise orange roughy
stock (part of ORH 3B) to the end of the 2007–08 fishing year. N Z Fish Assess
Rep 2009/15. Wellington: Ministry of Fisheries.
47. Robertson DA, Roberts PE, Wilson JB (1978) Mesopelagic faunal transition
across the Subtropical Convergence east of New Zealand. N Z J Mar Fresh Res
12: 295–312.
48. McClatchie S, Dunford A (2003) Estimated biomass of vertically migrating
mesopelagic fish off New Zealand. Deep Sea Res I 50: 1263–1281.
49. Coombs RF, Barr R (2007) In situ measurements of orange roughy (Hoplostethus
atlanticus) target strength. ICES J Mar Sci 64: 1220–1234.
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 11 November 2011 | Volume 6 | Issue 11 | e26704
50. Koslow JA, Kloser R, Stanley CA (1995) Avoidance of a camera system by a
deepwater fish, the orange roughy (Hoplostethus atlanticus). Deep-Sea Res I 42:
233–244.
51. Mauchline J (1980) The biology of mysids and euphausiids. Adv Mar Biol 18: l-
369.
52. Bolger T, Connolly PL (1989) The selectio n of suitable indices for the
measurement and analysis of fish condition. J Fish Biol 34: 171–182.
53. Henderson BA, Morgan GR (2002) Maturation of walleye by age, size and
surplus energy. J Fish Biol 61: 999–1011.
54. Morgan MJ (2004) The relationship between fish condition and the probability
of being mature in American plaice (Hipploglossoidea platessoides). ICES J Mar Sci
61: 64–70.
55. Pardoe H, Tho´rdarson G, Marteinsdo´ttir G (2008) Spatial and temporal trends
in condition of Atlantic cod Gadus morhua on the Icelandic shelf. Mar Ecol Prog
Ser 362: 261–277.
56. Rideout RM, Rose GA, Burton MP (2005) Skipped spawning in female
iteroparous fishes. Fish Fish 6: 50–72.
57. Anderson OF, Bagley NW, Hurst RJ, Francis MP, Clark MR, et al. (1998) Atlas
of New Zealand fish and squid distributions from research bottom trawls. NIWA
Tech Rep 42. Wellington: Ministry of Fisheries.
58. Porteiro FM, Sutton TT (2007) Midwater fish assembl ages and seamounts. In:
Pitcher TJ, Morato T, Hart PJB, Clark MR, Haggan N, Santos RS, eds.
Seamounts: Ec ology, Conservation and Man agement. Fish and Aquatic
Resources Series. Oxford: Blackwell. pp 101–116.
59. Hirch S, Christiansen B (2010) The trophic blockage hypothesis is not supported
by the diets of fishes on Seine seamount. Mar Ecol 31(Suppl 1): 107–120.
60. Clark MR, Rowden AA, Schlacher T, Williams A, Consalvey M, et al. (2010)
The Ecology of Seamounts: Structure, Function, and Human Impacts. Ann Rev
Mar Sci 2: 253–278.
Stock Structure in Orange Roughy
PLoS ONE | www.plosone.org 12 November 2011 | Volume 6 | Issue 11 | e26704
