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Fluctuations in sandeel biomass at Shetland: implications for the commercial fishery and breeding seabirds

Fishing vs. natural recruitment variation in sandeels
as a cause of seabird breeding failure at
Shetland: a modelling approach
Elvira S. Poloczanska, Robin M. Cook, Graeme D. Ruxton, and Peter J. Wright
Poloczanska, E. S., Cook, R. M., Ruxton, G. D., and Wright, P. J. 9999. Fishing vs. natural
recruitment variation in sandeels as a cause of seabird breeding failure at Shetland:
a modelling approach. eICES Journal of Marine Science, 61: 788e797.
Sandeels represent a major component in the diet of fish, bird, and mammal predators as
well as supporting a large industrial fishery. The availability of young sandeels in coastal
waters around Shetland is generally considered a key factor influencing the breeding
success of many seabird species in the area, but the risk to the populations as a direct
consequence of the fishery is unknown. Low exploitation rates coupled with high natural
mortality rates make assessment of the Shetland sandeel stock problematic and safe
biological limits have not yet been defined. We use stochastic models to evaluate the likely
effect of varying fishing mortality on kittiwake breeding success. The models consider some
main sources of uncertainty about natural processes, such as recruitment and natural
mortality, which may affect the design of robust management strategies. The type of model
tested had a stronger influence on sandeel recruitment than the level of fishing pressure.
Even with low exploitation rates, poor years for seabird breeding were inevitable.
Ó2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.
Keywords: kittiwake, sandeel, seabird breeding, Shetland.
Received 19 June 2003; accepted 23 March 2004.
E. S. Poloczanska and G. D. Ruxton: Division of Environmental and Evolutionary Biology,
Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow G12 8QQ,
Scotland, UK. Present address of E. S. Poloczanska: Scottish Association for Marine
Science, Dunstaffnage Marine Laboratory, Oban, Argyll PA37 1QA, Scotland, UK. R. M.
Cook and P. J. Wright: Fisheries Research Services, Marine Laboratory, Aberdeen AB11
9DB, Scotland, UK. Correspondence to E. S. Poloczanska: e-mail: elvira.poloczanska@
Sandeels (Ammodytes sp.) are small, shoaling fish that are
ubiquitous throughout the North Sea, including shallow
coastal waters over sandbanks (Macer, 1966; Reay, 1970).
The industrial fishery for sandeels in the North Sea is the
largest single-species fishery in this area. Sandeels are an
important part of the marine foodweb, forming a major
component in the diets of many fish, marine mammal, and
seabird species (Furness, 1990; Hammond et al., 1994;
Tollit and Thompson, 1996; Wright and Tasker, 1996;
Greenstreet et al., 1998; Doyle and Greenstreet, 1999).
The availability of young sandeels in the coastal waters
around Shetland appears to be a crucial factor influencing
local breeding success of many seabirds such as the kitti-
wake Rissa tridactyla (Danchin, 1992; Hamer et al., 1993),
Arctic skua Stercorarius parasiticus (Phillips et al., 1996)
and Arctic tern Sterna paradisea (Monaghan et al., 1989,
1992; Monaghan, 1992). The dependence of seabird
breeding on sandeels has resulted in conflict between the
fishing and environmental interests, most notably in the
Shetland area in the 1990s.
Around Shetland, juvenile sandeels recruit to the fishery
as 0-group fish (Wright, 1996). The fishing grounds are
close inshore and often adjacent to major breeding seabird
colonies (Monaghan, 1992). During the late 1980s, the
breeding success of the local seabird populations declined
concurrently with a decline in the sandeel population and
landings. At the time, there was considerable controversy
about the relationship between the fishery and the availabil-
ity of sandeels to breeding birds. Environmental groups
argued for a closure of the fishery as a precautionary
measure. Subsequent research indicated that the decrease in
the sandeel population was not caused by fishing but
by environmentally induced fluctuations in recruitment
(Kunzlik, 1989; Wright, 1996).
Compared with the total North Sea sandeel catch of 0.5
million to 1 million tonnes per year, the fishery around
ICES Journal of Marine Science, 61: 788e797 (2004)
1054-3139/$30.00 Ó2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.
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Shetland is very small, averaging approximately 20 000
tonnes annually before 1991 (ICES, 2000). Following a
peak in 1982 (52 000 tonnes), landings and recruitment
went into decline and the fishery was closed completely at
the end of the fishing season in 1990. After several years of
extremely low recruitment, the production of a large year
class in 1991 induced a recovery of the stock and the fishery
was reopened in 1995. Recruitment to the Shetland stock is
highly variable from year to year (ICES, 2000), but the
mechanisms behind recruitment variability are poorly
At present, the fishery operates with an annual Total
Allowable Catch (TAC) of 7000 tonnes and a closed season
during June and July to avoid competition with seabirds
during the chick-rearing season. However, landings since
1995 have been extremely low with only 1300 tonnes
landed in 2001. The low exploitation rate coupled with high
natural mortality makes stock assessment problematic and
safe biological limits have not been defined for this stock.
The ecological interactions between sandeels, their preda-
tors, and the fishery remain poorly understood.
Currently, the fishery is not regarded as a threat to breed-
ing seabirds, but an increase in catch may reduce sandeel
availability. It is assumed that high fishing activity would
raise the risk of breeding failure for local seabirds. By
constructing sandeel population models incorporating func-
tional responses for seabird populations, it should be pos-
sible to quantify the risk of breeding failure under different
exploitation rates and to design suitable fishery manage-
ment regimes. However, the processes involved are highly
uncertain and parameter estimates have to be based on as-
sumptions that are difficult to verify. To address this
problem, we developed a range of alternative population
models to reflect the uncertainty about processes such as
recruitment and natural mortality. The results of Monte
Carlo simulations for different models were compared
statistically. The performance of different management
regimes was evaluated in terms of consequences for the
sandeel stock, for the fishery, and for breeding seabirds.
Sandeel population biology
Recruitment to the Shetland stock is variable and is posi-
tively autocorrelated (n ¼24; r1¼0:54; p!0:05) (ICES,
2000;Figure 1). In biological time-series, positive auto-
correlations are considered indicative of environmental
forcing (Steele, 1985; Petchey et al., 1997). Trends in
spawning-stock biomass tend to follow recruitment trends
with a two-year lag (Wright, 1996).
Sandeels are relatively short-lived fish ( generally up to
eight years) compared to most exploited North Sea fish
species and become mature at two years (Macer, 1966;
Gauld and Hutcheon, 1990). Adults are thought not to
undertake extensive spawning migrations and during
spawning attach their eggs to sand grains on the seabed
(Reay, 1970; Warburton, 1982). At Shetland, larvae emerge
during the early spring, when the North Sea circulation is
largely wind-driven. Thus, larval transport by currents is
likely to vary among years (Wright and Bailey, 1996;
Proctor et al., 1998). It has been suggested that the low
recruitment around Shetland during the late 1980s was
caused by a reduction in the passive transport of pre-recruit
sandeels from spawning grounds elsewhere, such as Orkney
(Proctor et al., 1998). The size or frequency of potential
influx of pre-recruits, or of losses owing to export, remains
unquantified. Because fishing mortality is low (Fð1e3Þ!0:3)
compared to natural mortality rates (Mð1Þ¼1:2,
Mð2e7Þ¼0:6; ICES, 2000), fluctuations in stock size are
driven primarily by natural processes.
Seabird breeding biology
Seabird populations can respond to food availability in
various ways, including changes in adult survivorship, chick
growth, fledging success, and colony attendance (Cairns,
1987). Each of these parameters may be sensitive to a
1974 1977 1980 1983 1986 1989 1992 1995
SSB (000 tonnes)
Recruits (billions)
0 50 100 150 200 250 300
Figure 1. (a) Time-series of spawning-stock biomass (SSB) and
recruitment and (b) stock-recruitment plot (drawn line: Ricker
stock-recruitment model estimated from 10 000 bootstrap simu-
lations; broken lines: 95% confidence interval) for sandeels at
Shetland (data from ICES, 2000).
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particular range of prey availability values and integrates
food availability over a specific time scale. Seabirds, being
relatively long-lived, should be buffered against short
sporadic periods of adverse environmental conditions such
as low prey availability or bad weather by varying reproduc-
tive effort (Monaghan et al., 1989, 1992; Phillips et al., 1996).
At Shetland, the entire breeding season (arrival to
departure) for small seabirds lasts from March to September
(Furness, 1990) and overlaps with the sandeel fishery
operating from April to September (except for the closure
during June and July since 1995). Small surface feeding
seabirds, such as kittiwakes (Harris and Wanless, 1990;
Harris and Wanless, 1997; Rindorf et al., 2000) and Arctic
terns (Monaghan et al., 1989, 1992), are considered
particularly sensitive to fluctuations in prey availability
(Furness and Tasker, 2000), and breeding success (number
of chicks fledged per apparently occupied nest) of these may
be regarded as a useful indicator of sandeel biomass over
the breeding season. A relationship with sandeel abundance
has been proposed before for kittiwakes in North Sea areas
(Furness and Tasker, 1997, 2000).
Breeding success of kittiwakes for colonies around
Shetland over 1986e1993 was taken from Dunnet and
Heubeck (1995). Data for additional sites and years were
supplied by Martin Heubeck, University of Aberdeen.
Breeding success was plotted against four biomass measures
of sandeel availability: 0-group, 0+1-group, 1-group, and
0 to 7-group (Figure 2). Biomass was calculated as a multiple
of the number by age group and year estimated by stock
assessment and mean weight-at-age (ICES, 2000). To take
account of the influence of colony size on mean breeding
success, a weighted mean breeding success was determined
for each year and a logistic model was fitted through the data.
Young kittiwakes at two colonies, Kettleness and Eshaness,
have been subject to particularly high predation by great
skuas (Catharacta skua), leading to very few chicks being
fledged (Dunnet and Heubeck, 1995). All data for Kettleness
and 1993e1996 data for Eshaness were excluded from
analysis. Positive relationships between weighted mean
breeding success and sandeel abundance were found for all
four measures of biomass, but the relationship for 0+1-group
was strongest.
We used an age-structured population model for sandeels
linked to breeding success of kittiwakes being dependent on
juvenile biomass. Effects of the fishery were simulated by
changing fishing mortality on the sandeel stock. Uncertainty
was addressed by examining three different recruitment
models and two patterns of natural mortality.
Population structure
The model time-step is one year. Eight age classes of
sandeel were distinguished (0-group recruits through to 7+
fish). Individuals were transferred from one age class to
the next in January (the last class being a cumulative one).
The initial population (age 1 and older) conformed to the
estimated stock numbers on 1 January 1981 (ICES, 2000;
Table 1). Numbers of 0-group were calculated within
the model using a stock-recruitment relationship. All
sandeels Rage 2 are assumed to be mature and all 0- and
1-group to be immature. Spawning-stock biomass (S)
was calculated as the numbers of the mature part of
the population multiplied by weight-at-age in the stock
(Table 1).
Age-specific instantaneous fishing mortality rates (F) are
available from stock assessment (ICES, 2000). F was
decomposed into an age-specific selectivity effect (s
) and
a year-specific effect ( f
;Cook and Reeves, 1993):
Fay ¼safyð1Þ
Estimates of s
and f
and the associated covariance matrix
were obtained by fitting a fully separable model (Cook
and Reeves, 1993) over the years, 1981e1990 inclusive
(Table 1). In the simulations, stochastic values for s
selected from the covariance matrix using a multivariate
correlation random number generator.
Models were tested with three levels of fishing mortality:
, and F
based on spawning-stock biomass per
recruit values (Sissenwine and Shepherd, 1987; Jakobsen,
1992; Caddy and Mahon, 1995). F
corresponds to
a mortality rate that on average balances recruitment. At
, recruitment is sufficient to replace losses in only 10%
of the years, which may be regarded as a high-risk exploita-
tion strategy. At F
, recruitment exceeds the losses in
90% of the years, which is probably a safe exploitation
As for F, age-specific natural mortality rates (M) can also
be decomposed into an age effect (m
) and a year effect (k
Cook, 1993):
May ¼makyð2Þ
The m
are effectively the assumed age-specific natural
mortality rates if k ¼1(ICES, 2002a;Table 1). In some
simulations, interannual stochasticity was introduced into
M assuming positive autocorrelation as a worst-case
scenario. A first-order autoregressive model was used to
generate autocorrelated k
where f
is a constant equal to mean of k
(0.10), f
is the
first-order autoregressive coefficient (+0.5), k
is the
variable k
at time t, and 3
is a random variable drawn
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from the normally distributed residual distribution with
a mean of zero and standard deviation s
such that:
where s
is standard deviation of the m
values (0.276).
Because of uncertainty about the underlying mechanisms,
three stock-recruitment models were applied. The base case
was a Ricker (1954, 1975) model with gamma-distributed
error terms. Secondly, a Ricker model with autocorrelated
error terms was used to simulate environmental forcing on
recruitment variability. The last case considered was a Ricker
model with weak curvature near the origin as a ‘‘worst-case’
scenario. The stock-recruit data contain few observations in
the range of low stock sizes and therefore the shape of the
relationship near the origin cannot be reliably determined.
However, if the curvature near the origin had been over-
estimated in the base case, the model population would
appear more resilient to high exploitation rates than the
population in the real world would be. Recruitment variabil-
ity caused by emigration and immigration was assumed to be
incorporated in the residuals of the stock-recruit models.
Case 1
The Ricker stock-recruit model is widely used in fishery
science and is simple and robust:
R¼aS ebS
where R is recruitment, S is spawning-stock biomass, and
a and b are constants. The model is dome-shaped, implying
that density-dependent processes tend to dominate at high
values of spawning-stock biomass.
We assumed that the variability around the functional
relationship followed a gamma-distribution function instead
Kittiwake breeding success
Kittiwake breeding success
Biomass Biomass
0 50 100 150
0 50 100 150
0 50 100 150 200
0 100 200 300
(a) (b)
Figure 2. Kittiwake breeding success ( young fledged per apparently occupied nest) at Shetland (1986e1993 from Dunnet and Heubeck,
1995; 1994e1996 from Heubeck, pers. comm.) against measures of sandeel biomass from ICES (2000): (a) 0+1-group with fitted logistic
model; (b) 0-group; (c) 1-group; and (d) 0 to 7-group (+: mean breeding success estimated for individual colonies; ,: weighted mean
breeding success across all colonies per year).
Table 1. Input parameters for sandeel population model (based on
ICES, 2000; P
: initial population size in millions; ma: proportion
mature; w
: weight-at-age in grams; s
: ln age-specific selectiv-
ity effect of fishing and natural mortality, respectively).
Age P
ma W
0e0 0.746 2.4599 2.7371 0.8
1 23 279 0 3.095 2.1144 2.3163 1.2
2 8 328 1 5.409 2.0847 2.3905 0.6
3 3 637 1 8.585 1.8717 2.4813 0.6
4 1 543 1 11.143 2.0775 2.2935 0.6
5 747 1 13.705 2.1885 2.6955 0.6
6 380 1 15.605 2.4615 3.2034 0.6
7+ 517 1 21.254 2.1885 2.6955 0.6
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of the more commonly used lognormal distribution, because
the latter may result in bias if the assumption is not exactly
met or sample size is small (MacCall and Ralston, 2002).
The stock-recruit model assuming gamma-distributed
error terms (residuals distributed randomly and indepen-
dently ewhite noise) was fitted by minimizing the sum of
the deviances:
Dev ¼X
ln Ri
where R
is observed recruitment (billions) for a specific S
ˆiis predicted recruitment (billions) for the same S
, and n
is the number of observations i. The best-fit Ricker model
with 95% confidence intervals estimated from 10 000
bootstrap simulations is shown in Figure 1b.
In the stochastic version, a bootstrap stock-recruit data set
was generated for each simulation of n years. Parameters and
residuals of the fitted Ricker model were recorded and used to
calculate recruitment in each model year. Recruitment
variability was generated by randomly selecting a residual
from the bootstrapped data set with replacement to preserve
the parameter covariance matrix.
Case 2
As before, the parameters of the Ricker model and associated
residuals were determined by 10 000 bootstrap simulations.
Autocorrelation was introduced into the residual sets by a
two-stage process. Firstly, a first-order autoregressive model
was used to generate a random autocorrelated series with
a mean of 0 and standard deviation of 1 for each population
simulation of n years:
where y
is the observation at time t and b
is the auto-
correlation coefficient.
The log-transformed historical recruit time-series for
the Shetland sandeel stock was positively and strongly
(b1¼0:6) autocorrelated (red noise) at a lag of one year.
This value was selected here as representing strong forcing
on the time-series. Secondly, the bootstrap data sets were
ranked in ascending order. Values in each generated auto-
correlated series were assigned a rank position (ascending
order) without re-arranging the series. These were then used
to re-order the bootstrap sets so following the pattern in the
autocorrelated series.
Case 3
Bootstrap output for the model parameters and residuals
were generated as described for Case 1. A subset was then
selected from this output in which parameter a fell in the
lower quartile of the range of estimates. Recruitment
variability was generated by randomly selecting a residual
from the bootstrap data set.
Seabird breeding success
The fitted logistic model (Figure 2a) was used to identify
a range of sandeel biomasses at which kittiwake breeding
success may be poor, not to produce actual predictions of
breeding success. Management of the sandeel stock in the
North Sea applies a decision rule stating that all sandeel
fishing within 50 km of the UK coast is halted if the
breeding success of kittiwakes drops below 0.5 chicks per
apparently occupied nest for three consecutive years (ICES,
2002b). In the logistic model fitted to kittiwake breeding
success against 0+1-group sandeel biomass (Figure 1d),
a breeding success R0.5 was obtained when biomass was
R60 000 tonnes and this value was selected as the
‘‘threshold’’ biomass to indicate failure of kittiwake breed-
ing. In the historical data set, the biomass of 0+1-group
dropped below this value in 7 out of 24 years (1986e1990
inclusive, 1993 and 1995; ICES, 2000).
Output statistics and simulations
The selected summary statistics to compare model output
were mean annual recruitment and SSB to describe the
sandeel population status, and mean catch to describe
fishery performance. In addition, two output statistics were
calculated for potential effects on seabird populations: the
number of years in a simulation with 0+1-group biomass
below the threshold and the percentage of simulations
producing series of three or more consecutive years with
0+1-group biomass below the threshold.
Simulations were run with a burn in a period of 100
years, without stochasticity, to reach equilibrium. Thereaf-
ter, stochasticity was switched on and a further 20 years
were simulated. Output statistics were calculated from the
stochastic period. To determine necessary sample size to
infer stability in results, the running total mean (mean of the
simulation means) for each output statistic was calculated
during 5000 consecutive simulations. Because these means
stabilized rapidly after about 1000 runs, we inferred that
5000 simulations was an adequate number. Simulations
with and without stochasticity in natural mortality were
carried out for each of the three levels of fishing mortality
and for each of the three recruitment models (18 trials).
The mean output statistics from the 18 sets of simulations
are shown in Table 2. The sandeel population was
considered to have ‘‘crashed’’ if recruitment, and thus
SSB, dropped to (near) zero for a number of consecutive
years and showed no indication of increasing again. The
incidence of crashing varied among F levels, being highest
at F
(17e20%), and was slightly higher among
recruitment models with low curvature near the origin
(Case 3). Even for F
, simulations still crashed in 1e3%
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of trials. In calculating output statistics, simulations with
population crashes were excluded.
Taking the output of the model with stochastic recruit-
ment (Case 1), F
, and constant M as the baseline, the
percentage change in sandeel summary statistics for the
other models was calculated relative to the baseline values
(Figure 3). Introducing autocorrelation in the recruitment
residuals (Case 2), using stock-recruit models with low
curvature near the origin (Case 3) or autocorrelating M
values all decreased mean recruitment. The largest declines
in mean recruitment, at all levels of F tested, occurred in
Case 3 and with autocorrelated M. Spawning-stock biomass
(SSB) decreased at F
and increased at F
SSB was more affected by autocorrelated M than by
constant M while the reverse was true for F
Mean catch increased with increased F by 33e41% in all
models except in Case 3, where the increase was only
8e13%. In Case 3 and with constant M, F
resulted in a larger average catch than F
. Reducing
fishing pressure decreased mean catch by 38e47% in all
models (Figure 3).
All models showed a high incidence of years with
juvenile sandeel biomass below the threshold for successful
seabird breeding. This happened on average during 3.3e5.9
years out of the 20 years simulated (Table 2). The
recruitment model with autocorrelated residuals (Case 2)
tended to produce runs with a higher incidence of years
below the threshold and this is also the case for
incorporating autocorrelation in M (Figure 4).
For all models, at least 22% of simulations (excluding
crashed simulations) contained at least one period of three
or more consecutive years below the threshold. Values were
highest for models with autocorrelated recruitment resid-
uals at all three levels of F. Introducing autocorrelation in
M also greatly increased the incidence of such events for all
recruitment models at all F.
One of the main concerns in managing the Shetland sandeel
fishery is the potential impact on breeding birds. The
simulations suggest that even with the most benign sandeel
population dynamics model and low F, the probability of
poor seabird breeding success would be once every six
years (3.4 years in a 20-year period). Using conventional
criteria used in management elsewhere in the North Sea, the
fishery would have to be closed during a 20-year period in
22% of runs for the most optimistic model. What is of
particular relevance, therefore, is the marginal increase in
the need to close the fishery as F increases. The simulations
indicate that for any of the stock-recruitment and natural
mortality models considered, events requiring fishery
closures increase marginally as F increases from F
, but that the probability of the sandeel stock collapsing
rises sharply (Table 2). This result would suggest that the
need to protect the sandeel stock from collapse would occur
before the threat to seabird breeding was itself elevated.
Our results illustrate the importance of understanding the
mechanisms driving recruitment for managing the sandeel
fishery. Models of recruitment processes are based on our
perceptions of underlying mechanisms, and the type of
model chosen strongly influences predictions of population
fluctuations and persistence. Generally, stock-recruit mod-
els incorporate random variation such that each randomized
value is assumed to be independent of other values.
However, this is rarely the case in biological systems as
the effects of environmental stochasticity may persist longer
than one season or one year (Moran, 1953; Grenfell et al.,
1998; Thompson and Ollason, 2001).
Positive autocorrelation noise in a single-species pop-
ulation model has been shown to decrease extinction risk
because a random value at time t is likely to be similar to
the value drawn at t 1(Ripa and Lundberg, 1996).
However, autocorrelating the residuals around the stock-
recruit curve (Case 2) seemed to have little influence on the
Table 2. Summary statistics from stochastic simulations of the
Shetland sandeel stock for 18 different models: Cases 1, 2, and 3:
recruitment (R) models with random gamma-distributed error
terms, autocorrelated error terms, and weak curvature at the origin,
respectively; Ct, A: constant and autocorrelated natural mortality
(M); Med, High, and Low: instantaneous fishing mortality rates
(F); %cr: percentage simulations where the population ‘‘crashed’
(see text); R, SSB, C: mean values for recruitment (billions),
spawning-stock biomass (!10
tonnes), catch (!10
respectively (means for 20-year simulations, excluding crashed
populations); y, %3y: mean number of years per 20-year
simulation, and percentage of simulations with R3 consecutive
years, having juvenile sandeel biomass below threshold value,
respectively (bold: baseline).
Model specification Output statistics
R M F %cr R SSB C y %3y
Case 1 C Med 6 73 G18 134 G45 18 G16 3.4 22
High 17 74 G22 115 G50 25 G17 3.9 27
Low 1 71 G14 150 G37 11 G12 3.3 22
A Med 7 68 G19 146 G59 19 G17 4.5 42
High 17 69 G22 124 G61 26 G19 4.6 40
Low 2 66 G17 162 G55 11 G14 4.5 44
Case 2 C Med 6 70 G16 130 G44 17 G15 4.7 54
High 17 71 G19 113 G47 24 G17 4.9 55
Low 1 69 G13 146 G37 11 G13 4.4 53
A Med 7 66 G17 142 G59 18 G16 5.6 67
High 17 66 G20 121 G59 25 G18 5.9 66
Low 3 65 G16 160 G55 11 G13 5.5 67
Case 3 C Med 8 65 G18 125 G47 20 G12 4.0 28
High 20 63 G20 106 G63 20 G13 4.6 34
Low 2 66 G16 141 G49 10 G10 3.5 23
A Med 8 60 G18 136 G63 15 G13 5.0 45
High 20 58 G19 116 G41 21 G14 5.5 46
Low 2 60 G16 154 G59 10 G11 4.6 42
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number of simulations considered to have crashed (become
extinct) or on the simulated population parameters compared
to the base case. This does have strong implications for
breeding seabirds. The frequency of years of juvenile sandeel
biomass below the threshold set for ‘‘sufficient’’ seabird
breeding success increased when the residuals around the
stock-recruit curve were positively autocorrelated. As resid-
uals below the curve are likely to occur in sequence, this
produces a series of years of low recruitment to the sandeel
population. This explains why more trials met closure condi-
tions in stochastic simulations of 20 years. Similarly, in-
troducing positive autocorrelation in natural mortality rates
increased the frequency of years with conditions for poor
seabird breeding success.
Evidence suggests that recruitment to the Shetland sandeel
stock may be largely driven by hydroclimatic factors. Wright
(1996) suggested that the poor recruitment during the late
1980s may have been caused by unfavourable oceanic
currents reducing larval advection to Shetland. Analysis of
North Sea data from the Continuous Plankton Recorder
indicated a large anomalous period during these years,
signifying unusual ocean-climate conditions (Edwards et al.,
2002). Furthermore, Arnott and Ruxton (2002) showed
a relationship between recruitment to North Sea sandeels and
the winter index of the North Atlantic Oscillation (NAO).
Fluctuations in large-scale climatic phenomena, such as
NAO, tend to persist over long time scales (decades or more).
Fluctuations in the NAO have been linked with timing of
biological events and/or population dynamics in marine
(Planque and Taylor, 1998; Sims et al., 2001; Attrill and
Power, 2002), freshwater (Straile, 2000; Gerten and Adrian,
2001), and terrestrial (Post and Forchhammer, 2002; Huppop
-30 -20 -10 0 10 20 30
F Low
F High
F Med
-50 -30 -10 10 30 50
-30 -20 -10 0 10 20 30
% change from baseline
Figure 3. Percentage change in sandeel summary statistics (R: recruitment; SSB: spawning-stock biomass; and C: catch) from baseline
model for three recruitment models (Case 1: 1/2; Case 2: 3/4; Case 3: 5/6) and two natural mortality models (constant M: 1/3/5;
autocorrelated M: 2/4/6) at three levels of fishing mortality (F
, and F
794 E. S. Poloczanska et al.
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and Huppop, 2003; Post, 2003) systems. The positive
autocorrelation in the Shetland sandeel recruit time-series
and apparent synchrony in recruitment across Shetland
fishing grounds suggests that environmental forcing may be
strong (Poloczanska, 2001).
The set of recruitment models with low curvature near
the origin (Case 3) had the highest rates of population
crashes, as the incidence of high recruitment was reduced
for any given SSB. Determining a realistic stock-recruit
relationship is impossible with a limited data set over
1 3 5 7 9 1113151719
M Stochastic
M Constant
Frequency %
Years below threshold Years below threshold
Figure 4. Frequency distribution of simulations with 1e20 years of juvenile sandeel biomass below the threshold value for three
recruitment models (Case 1: 1/2; Case 2: 3/4; Case 3: 5/6) and two natural mortality models (constant M: 1/3/5; autocorrelated M: 2/4/6) at
three levels of fishing mortality (F
, and F
), excluding simulations where sandeel populations crashed.
795A cause of seabird breeding failure at Shetland
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a restricted SSB range. Choosing a wrong model may over-
predict the range of potential recruitment for any given SSB
and might make the simulated population appear more
resilient to exploitation. However, our results show that
allowing for autocorrelation in the recruitment residuals has
much more impact on most summary statistics than chang-
ing the curvature parameter (Case 2;Table 2).
If fluctuations in the sandeel stock at Shetland are driven
largely by extraneous factors, this has important implica-
tions for fishery management. Even if exploitation rates are
kept low, the risk of population crash exists. The risk
increases with exploitation pressure. Of the simulations that
did not crash, the type of model selected had stronger
influence on sandeel recruitment and, as a derivative, seabird
breeding success than fishing pressure. The frequency of
years with sandeel biomass below the threshold increased
with exploitation rate, but the occurrence of a series of three
consecutive low years did not necessarily increase. Thus, the
management system evaluated here would not seem to
require additional measures on controlling fishing pressure
to limit future closures to the fisheries. Poor breeding years
for seabirds are inevitable, even with a greatly reduced
fishery. It is up to seabird ecologists to understand the
implications of breeding failure years on the long-term
dynamics of seabird populations.
The study was carried out as part of a PhD funded by the
Fisheries Research Services. Thanks to Martin Heubeck
for supplying data and advice on kittiwake breeding at
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Full-text available
A major debate in ecology concerns the relative importance of intrinsic factors and extrinsic environmental variations in determining population size fluctuations. Spatial correlation of fluctuations in different populations caused by synchronous environmental shocks,, is a powerful tool for quantifying the impact of environmental variations on population dynamics,. However, interpretation of synchrony is often complicated by migration between populations,. Here we address this issue by using time series from sheep populations on two islands in the St Kilda archipelago. Fluctuations in the sizes of the two populations are remarkably synchronized over a 40-year period. A nonlinear time-series model shows that a high and frequent degree of environmental correlation is required to achieve this level of synchrony. The model indicates that if there were less environmental correlation, population dynamics would be much less synchronous than is observed. This is because of a threshold effect that is dependent on population size; the threshold magnifies random differences between populations. A refined model showsthat part of the required environmental synchronicity can be accounted for by large-scale weather variations. These results underline the importance of understanding the interaction between intrinsic and extrinsic influences on population dynamics.
Full-text available
SUMMARY It is accepted that accurate estimation of risk of population extinction, or persistence time, requires predic- tion of the e¡ect of £uctuations in the environment on population dynamics. Generally, the greater the magnitude, or variance, of environmental stochasticity, the greater the risk of population extinction. Another characteristic of environmental stochasticity, its colour, hasbeen found to a¡ect population persis- tence. This is important because real environmental variables, such as temperature, are reddened or positively temporally autocorrelated. However, recent work has disagreed about the e¡ect of reddening environmental stochasticity. Ripa & Lundberg (1996) found increasing temporal autocorrelation (reddening) decreased the risk of extinction, whereas a simple and powerful intuitive argument (Lawton 1988) predicts increased risk of extinction with reddening. This study resolves the apparent contradiction, in two ways, ¢rst, by altering the dynamic behaviour of the population models. Overcompensatory dynamics result in persistence times increasing with increased temporal autocorrelation; undercompensa- tory dynamics result in persistence times decreasing with increased temporal autocorrelation. Secondly, in a spatially subdivided population, with a reasonable degree of spatial heterogeneity in patch quality, increasing temporal autocorrelation in the environment results in decreasing persistence time for both types of competition.Thus, the inclusion of coloured noise into ecological models can have subtle interac- tions with population dynamics.
(1) Data on breeding success from thirty-five British and one Irish kittiwake Rissa tridactyla colony were collected between 1986 and 1988. (2) At each colony the mean number of young fledged per completed nest was similar in 1986 and 1987, but at North Sea colonies success was significantly poorer in 1988. (3) Colonies in the northern North Sea showed a negative relationship between breeding success and latitude in 1986 and 1987, whilst in 1988 there was a significant north-south trend in success over the whole length of eastern Britain with colonies in the south being more successful than those in the north. There was no similar pattern amongst west coast colonies. (4) In North Sea colonies most breeding failures occurred at the chick stage, but on the west coast some birds did not lay whilst others failed during incubation or soon after hatching. (5) Amongst North Sea colonies it seemed likely that food shortage during chick rearing was responsible for the low breeding success. This conclusion was consistent with the independent finding that a series of poor recruiting year classes had caused a decline recently in the Shetland stock of sandeels Ammodytes spp.
1. Faecal material collected in January, June, August and November 1985 was used to investigate seasonal and regional variation in grey seal diet around the Western Isles of Scotland. A total of 511 individual faex was obtained from the Outer and 238 from the Minch/Inner Hebrides; 79% and 67%, respectively, contained fish otoliths and/or cephalopod beaks. 2. Fish otoliths and cephalopod beaks were identified, and measured to estimate diet composition by weight. The degree of otolith erosion due to digestion was accounted for using empirically derived digestion coefficients. 3. Gadids dominated the diet, contributing approximately 40% or greater in percentage by weight. The gadid species most commonly found were ling, cod and whiting, the predominance of which varied by area and season. 4. The main flatfish in the diet were plaice and megrim in the Outer Hebrides, and witch in the Minch/Inner Hebrides. The dominant species again varied by area and season. 5. The contribution of sandeels to the diet in the Hebrides was less than that found in other areas of Britain (Orkney and the east coast). Pelagic schooling fish (herring, mackerel and horse mackerel) contributed more to the diet than in other areas of Britain. 6. The significant variation in sandeel occurrence by area (many more sandeels taken off the Outer than the Minch/Inner Hebrides) can be explained by the distribution of the sandeel's preferred sea bed type (smooth areas of gravelly sand) in the region. However, two species of flatfish, megrim and witch, were most prevalent in the diet in areas which did not correspond to their supposed preferred sea bed depth or sediment type. 7. The greater number of cod and ling taken in summer compared to the rest of the year may compensate for the observed absence of an increase in sandeel consumption, seen at this time of year in other areas of Britain.
Five cliff-nesting species were identified as being particularly vulnerable, namely fulmar (Fulmarus glacialis), shag (Phalacrocorax aristotelis), kittiwake (Rissa tridactyla), guillemot (Uria aalge) and razorbill (Alca torda). Eider (Somateria mollissima) was also surveyed. On many cliffs all five of these species nest together and the choice of colonies took this into account for logistic reasons. The data are presented in several ways: numbers, actual numbers of items counted; annual means, whereby at any colony a count will be made in each sample plot on a number of different days, usually during June of each year, for each data the plot counts being summed to provide a "colony count'; and an index in which in order to consider trends of population change over Shetland as a whole the data for each colony have been converted to an "Index'. -after Authors
The hypothesis that animal population dynamics may be synchronized by climate is highly relevant in the context of climate change because it suggests that several populations might respond simultaneously to climatic trends if their dynamics are entrained by environmental correlation. The dynamics of many species throughout the Northern Hemisphere are influenced by a single large-scale climate system, the North Atlantic Oscillation (NAO), which exerts highly correlated regional effects on local weather. But efforts to attribute synchronous fluctuations of contiguous populations to large-scale climate are confounded by the synchronizing influences of dispersal or trophic interactions. Here we report that the dynamics of caribou and musk oxen on opposite coasts of Greenland show spatial synchrony among populations of both species that correlates with the NAO index. Our analysis shows that the NAO has an influence in the high degree of cross-species synchrony between pairs of caribou and musk oxen populations separated by a minimum of 1,000 km of inland ice. The vast distances, and complete physical and ecological separation of these species, rule out spatial coupling by dispersal or interaction. These results indicate that animal populations of different species may respond synchronously to global climate change over large regions.
Large-scale climatic fluctuations, such as the El Nino Southern Oscillation and the North Atlantic Oscillation (NAO), are known to influence variability in abiotic site conditions and organism population dynamics in both terrestrial and aquatic ecosystems. Here we demonstrate that the influence of the NAO on lake water temperatures-one of the major factors controlling ecological processes in lakes-differs substantially among lake types of different thermal structures and mixing regimes, even under identical climatic forcing. A frequently circulating polymictic lake was found to be least influenced by the winter effects of the NAO, with an effect lasting only into early spring. In contrast, in a deep dimictic lake with stable summer stratification, the NAO signal persisted in the hypolimnion until the following winter. A shallow dimictic lake revealed an intermediate response, as weather conditions both in April and midsummer probably modified the strength and persistency of the NAO signal in the hypolimnion of that lake. Based on these results, it is to be expected that NAO effects on ecological processes vary significantly among lakes. Because the study period (1979-1998) includes a series of uncommonly warm winter and spring seasons, our findings also suggest that the influence of anticipated climate warming will vary substantially among lake types.