r eports climate change and distribution e shifts …faculty.bennington.edu/~sherman/the ocean...
TRANSCRIPT
Climate Change and DistributionShifts in Marine Fishes
Allison L. Perry,1* Paula J. Low,2. Jim R. Ellis,2 John D. Reynolds1*
We show that the distributions of both exploited and nonexploited North Seafishes have responded markedly to recent increases in sea temperature, withnearly two-thirds of species shifting in mean latitude or depth or both over 25years. For species with northerly or southerly range margins in the North Sea,half have shown boundary shifts with warming, and all but one shifted north-ward. Species with shifting distributions have faster life cycles and smallerbody sizes than nonshifting species. Further temperature rises are likely to haveprofound impacts on commercial fisheries through continued shifts in distribu-tion and alterations in community interactions.
Climate change is predicted to drive species
ranges toward the poles (1), potentially result-
ing in widespread extinctions where dispersal
capabilities are limited or suitable habitat is
unavailable (2). For fishes, climate change
may strongly influence distribution and abun-
dance (3, 4) through changes in growth, sur-
vival, reproduction, or responses to changes at
other trophic levels (5, 6). These changes may
have impacts on the nature and value of com-
mercial fisheries. Species-specific responses are
likely to vary according to rates of population
turnover. Fish species with more rapid turnover
of generations may show the most rapid
demographic responses to temperature changes,
resulting in stronger distributional responses to
warming. We tested for large-scale, long-term,
climate-related changes in marine fish distribu-
tions and examined whether the distributions of
species with fast generation times and asso-
ciated life history characteristics are partic-
ularly responsive to temperature changes.
We studied the demersal (bottom-living)
fish assemblage in the North Sea. This group
is composed of more than 90 species with
varied biogeographical origins and distribu-
tion patterns. North Sea waters have warmed
by an average of 0.6-C between 1962 and
2001, based on four decadal means before
2001, and by 1.05-C from 1977 to 2001 (7),
which correspond with our fish survey time
series. Survey data were used to calculate
catch per unit effort to determine centers of
abundance (mean latitudes and depths) for
all species and boundary latitudes for those
species that have either northerly or souther-
ly range limits in the North Sea (7). No
species range was entirely confined to the
North Sea. Measures of distribution were
regressed against same-year and time-lagged
bottom temperatures, and also a composite
measure of temperatures, the North Atlantic
Oscillation Index, the Gulf Stream Index, and
the ratio of abundances of northern and south-
ern calanoid copepod species (7). We also con-
trolled for changes in abundance that may
have influenced species distributions (7).
Centers of distribution as measured by
mean latitudes shifted in relation to warming
for 15 of 36 species (Table 1). These trends
were shown by both commercially exploited
species Esuch as Atlantic cod (Gadus morhua)
and the common sole (Solea solea)^, and by
species that are not targeted by fisheries Esuch
as scaldfish (Arnoglossus laterna) and snake-
blenny (Lumpenus lampretaeformis)^. Distances
moved ranged from 48 to 403 km (average
distance x 0 172:3 T 98:8 km, n 0 15 spe-
cies) (Fig. 1) and most of these shifts (13 of 15)
were northward (Table 1). The spatial tem-
perature gradient of the North Sea is some-
what unusual; water temperatures become
colder with increasing latitude in the south-
ern North Sea but become slightly warmer
with increasing latitudes in the north (8),
where warm North Atlantic Current waters
enter the region (9). This temperature pattern
may explain one of the two exceptional spe-
cies that moved south, the Norway pout
(Trisopterus esmarkii). Its distribution was
centered in the northern North Sea, and its
southern movement brought it into cooler
waters. The other exception was the com-
mon sole. We speculate that the southward
shift in its distribution may have been
caused by the fact that the cleanup of the
Thames estuary led to its emergence as a
major sole nursery ground during the study
period (10).
Most species that showed climate-related
latitudinal changes also shifted in depth, which
was unsurprising because North Sea depths are
roughly positively correlated with latitude (8).
A further six species, including plaice (Pleuro-
nectes platessa) and cuckoo ray (Leucoraja
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Fig. 1. Examples of North Sea fish distribu-tions that have shifted north with climaticwarming. Relationships between mean lati-tude and 5-year running mean winter bot-tom temperature for (A) cod, (B) anglerfish,and (C) snake blenny are shown. In (D), rangesof shifts in mean latitude are shown for (A),(B), and (C) within the North Sea. Bars onthe map illustrate only shift ranges of meanlatitudes, not longitudes. Arrows indicatewhere shifts have been significant over time,with the direction of movement. Regressiondetails are in Table 1.
1Centre for Ecology, Evolution and Conservation, Schoolof Biological Sciences, University of East Anglia, NorwichNR4 7TJ, UK. 2Centre for Environment, Fisheries andAquaculture Science, Lowestoft Laboratory, LowestoftNR33 0HT, UK.
*To whom correspondence should be addressed.E-mail: [email protected] (A.L.P.); [email protected](J.D.R.)..Present address: University Marine Biological StationMillport, Isle of Cumbrae KA28 0EF, UK.
R E P O R T S
24 JUNE 2005 VOL 308 SCIENCE www.sciencemag.org1912
naevus), moved deeper with warming but did
not change in latitude, suggesting that they may
have responded to climatic variation through
local movements offshore or into pockets of
deeper water. Considering both latitude and
depth, nearly two-thirds of species (n 0 21 out
of 36) have shown distributional responses to
climatic warming (table S1).
We tested whether species boundaries have
also been displaced by warming, by exam-
ining those 20 species from our data set with a
southern or a northern range limit in the North
Sea. The boundaries of half of these fishes
moved significantly with warming (Fig. 2 and
table S2). Southern boundaries shifted in 6 of
12 cases, and all shifts were northward. Four
of eight northern boundaries also moved with
warming. All but one of these species shifted
north, despite the fact that their northern range
limits lay in the relatively intensively fished
southern North Sea (11). Shifting species again
included both exploited and nonexploited fishes.
Boundaries moved over distances ranging from
119 to 816 km (x 0 304 T 196 km, n 0 10),
with the highest value describing the range
of movement of the southern boundary of
blue whiting (Micromesistius poutassou),
which is the target of the largest fishery in
the Atlantic (12). In the case of bib (Trisopte-
rus luscus), the northern boundary shifted by
342 km from 1978 to 2001, a trend that is
supported by observations that North Sea
catches of this species have been increasing
(13).
To identify shifts that may have been
driven by fishing or other nonclimatic influ-
ences, we also examined distribution changes
over time. Fishing pressure could not be in-
cluded explicitly in our analyses because reli-
able fishing effort data on a comparable spatial
and temporal scale do not exist for the North
Sea. However, during at least the last decade
of the 25-year period of analysis, the spatial
distribution of effort remained relatively con-
stant (11), and total fishing effort may have
declined slightly (14). Temporal trends in dis-
tribution suggested that fishing alone could not
explain climate-related shifts; despite the gen-
eral increase in temperature over the study
period, warming-related shifts occurred inde-
pendently of time for centers of distribution
in 8 of 36 species and for range limits in 4 of
20 species (table S3). Such shifts may have
reflected year-to-year environmental variabil-
ity, with northward movement during warm
years cancelled by southward movement dur-
ing cool years. If so, long-term distribution
shifts could depend strongly on future climat-
ic variability, in addition to longer-term av-
erage conditions.
The examination of temporal trends also
allowed for rough comparisons to be drawn
with rates of warming-related distribution shifts
in other taxa. A recent meta-analysis of climate-
change impacts on natural systems estimated
the mean annual rate of boundary movement
for 99 species of birds, butterflies, and alpine
herbs at 0.6 km northward or 0.6 m upward
(1). From the current study, the mean rate of
movement for the six fish species whose
boundaries shifted in relation to both climate
and time Ebib, blue whiting, lesser weever
Table 1. Statistically significant multiple regressions of the effects of threemeasures of North Sea warming on mean latitudes of 36 demersal fishes from1977 to 2001. PC1, first principal component from principal components anal-
ysis (PCA) of eight environmental variables (PC1 generally describes warming).Winter temp. and summer temp. indicate 5-year running mean bottom tem-peratures for December to March and June to September, respectively.
Species Common name dfMean
latitude(-N)
SD PC1 r2 PWintertemp.
r2 PSummer
temp.r2 P
Agonus cataphractus Pogge 22 54.67 0.90Anarhichus lupus Atlantic wolffish 21 58.14 0.46Argentina spp. Argentines 24 59.59 0.30Arnoglossus laterna Scaldfish 15 54.17 0.31 0.456 0.43 0.006Buglossidium luteum Solenette 23 54.14 0.28Callionymus lyra Dragonet 23 55.40 0.65 0.265 0.16 0.049 0.937 0.34 0.002Echiichthys vipera Lesser weever 24 53.30 0.13 0.191 0.39 0.001Eutrigla gurnardus Grey gurnard 23 56.13 0.35 0.194 0.30 0.006 0.651 0.61 G0.001 0.402 0.17 0.040Gadiculus argenteus Silvery pout 23 59.83 0.41Gadus morhua Atlantic cod 23 56.81 0.34 0.256 0.58 G0.001 0.534 0.38y G0.001 0.578 0.33y G0.001Glyptocephalus cynoglossus Witch 24 58.22 0.42Hippoglossoides platessoides Long rough dab 24 57.62 0.21 0.304 0.40 0.001Lepidorhombus boscii Fourspot megrim 24 60.51 0.37Leucoraja naevus Cuckoo ray 19 58.06 0.57Limanda limanda Dab 24 55.86 0.13 0.180 0.35y 0.001Lophius piscatorius Anglerfish 23 57.99 0.58 0.254 0.19 0.032 0.818 0.37 0.001Lumpenus lampretaeformis Snake blenny 12 56.52 1.15 3.174 0.81 G0.001Melanogrammus aeglefinus Haddock 24 57.91 0.16Merlangius merlangus Whiting 23 56.57 0.15 0.066 0.19 0.034Merluccius merluccius Hake 24 58.84 0.59Micromesistius poutassou Blue whiting 21 60.13 0.48Microstomus kitt Lemon sole 24 57.06 0.24Molva molva Ling 24 59.26 0.74Myxine glutinosa Hagfish 11 57.51 0.62Pleuronectes platessa Plaice 24 55.52 0.18Pollachius virens Saithe 24 59.44 0.20Psetta maxima Turbot 13 54.73 0.31Rhinonemus cimbrius Four-bearded rockling 22 56.05 0.68 0.419 0.40 0.001 1.147 0.53 G0.001 0.950 0.28 0.008Scyliorhinus canicula Small-spotted catshark 20 58.34 0.89Sebastes spp. Redfish 18 59.89 0.49Solea solea Common sole 13 53.68 0.66 –0.941 0.38 0.020 –0.963 0.34 0.028Squalus acanthias Spurdog 19 56.29 0.68Trigla lucerna Tub gurnard 19 53.89 0.50Trisopterus esmarkii Norway pout 23 58.59 0.26 –0.190 0.52 G0.001 –0.304 0.25 0.010 –0.429 0.37 0.001Trisopterus luscus Bib 9 53.29 0.51 0.489* 0.45 0.035Trisopterus minutus Poor cod 23 55.63 0.66 0.334 0.26 0.012 0.877 0.33 0.003 0.753 0.18 0.035
*A relationship with annual mean summer or winter temperature. .To identify the proportion of variance in distribution accounted for by warming, r2 and P describe the squaredsemi-partial correlation coefficient, where abundance was also a significant predictor of distribution.
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www.sciencemag.org SCIENCE VOL 308 24 JUNE 2005 1913
(Echiichthys vipera), Norway pout, scald-
fish, and witch (Glyptocephalus cynoglos-
sus)^ was 2.2 km per year. It is perhaps
unsurprising that the rate of shift might be
higher for marine fishes than for alpine herbs
and butterflies, given that marine fish may
generally face fewer constraints on movement.
However, if such a difference is indicative of
more widespread trends in marine fishes, cli-
mate change could pose a greater threat to
fish populations that are constrained by their
dispersal capabilities or habitat requirements.
If the differences in rates of movement
among the taxa documented here result from
differential rates of population turnover, we
would expect species with life history traits
associated with fast population growth to have
responded most strongly to climate change. To
test this prediction, we compared life history
traits between shifting and nonshifting species
(7). As predicted, shifting species tend to have
faster life histories than do nonshifting species,
with significantly smaller body sizes, faster
maturation, and smaller sizes at maturity
(Fig. 3). Body growth rates did not differ
significantly between shifting and non-
shifting species (P 0 0.19). These relation-
ships therefore provide a starting point for
predicting species_ responses to future climate
change. These predictions could be refined,
through detailed studies of the relative sensitiv-
ities of different life history stages, to uncover
the specific mechanisms driving the patterns.
Our study shows that climate change is
having detectable impacts on marine fish dis-
tributions, and observed rates of boundary
movement with warming indicate that future
distribution shifts could be pronounced. Mean
annual surface temperatures in the North Sea
are predicted to increase by 0.5 to 1.0-C by
2020, 1.0 to 2.5-C by 2050, and 1.5 to 4.0-Cby 2080 (15). We used the midpoints of
these temperature ranges as the basis for a
rough approximation, which suggested that
two types of commercial fishes, blue whiting
and redfishes (Sebastes spp.), may retract
completely from the North Sea by 2050, and
by 2080, bib may extend its range northward
to encompass the entire region. Such changes
will clearly also depend on the responses of
their predators and prey to increases in bottom
temperature and on the availability of suitable
habitat.
These findings may have important im-
pacts on fisheries. For example, species with
slower life histories are already more vul-
nerable to overexploitation (16–18) and may
also be less able to compensate for warming
through rapid demographic responses. A fur-
ther concern is that differential rates of shift
could result in altered spatial overlap among
species, thereby disrupting interactions and
also potentially compounding the decoupling
effects of climate-driven changes in phenol-
ogy (19). Previous work off the eastern United
States has shown that fishes with the most
temperature-sensitive distributions included
key prey species of nonshifting predators (20).
Such changes could have unpredictable ef-
fects in an ecosystem already under heavy
anthropogenic pressure.
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Fig. 3. Differences in life-history traits between shifting (n 0 15) and nonshifting (n 0 21) specieswith respect to centers of distribution (mean latitudes). (A) Maximum body size [t 0 –2.41, degreesof freedom (df) 0 34, P 0 0.02]. (B) Age at maturity (t 0 –2.86, df 0 27, P 0 0.01). (C) Length atmaturity (t 0 –2.29, df 0 29, P 0 0.03). Means are shown with standard errors.
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Shift rate (km/°C) Shift rate (km/y)
Fig. 2. Frequency distributions of fish species shift rates in relation to warming and time. (A) Ratesof shift for northerly species’ (southern) boundaries with climate. (B) Southerly species’ (northern)boundaries with climate. (C) All species’ mean latitudes with climate. (D) Northerly species’(southern)boundaries over time. (E) Southerly species’ (northern) boundaries over time. (F) All species’ meanlatitudes over time. Rates for shifting species are slopes from regressions.
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24 JUNE 2005 VOL 308 SCIENCE www.sciencemag.org1914
References and Notes1. C. Parmesan, G. Yohe, Nature 421, 37 (2003).2. C. D. Thomas et al., Nature 427, 145 (2004).3. C. M. Wood, D. G. McDonald, Eds., Global Warming:
Implications for Freshwater and Marine Fish (Cam-bridge Univ. Press, Cambridge, 1997).
4. K. Brander et al., Int. Counc. Explor. Sea Mar. Sci.Symp. 219, 261 (2003).
5. G. Beaugrand, K. M. Brander, J. A. Lindley, S. Souissi,P. C. Reid, Nature 426, 661 (2003).
6. G. Beaugrand, P. C. Reid, F. Ibanez, J. A. Lindley, M.Edwards, Science 296, 1692 (2002).
7. Materials and methods are available as supportingmaterial on Science Online.
8. R. J. Knijn, T. W. Boon, H. J. L. Heessen, J. F. G. Hislop,Atlas of North Sea Fishes [International Council forthe Exploration of the Sea (ICES) CooperativeResearch Report 194, ICES, Copenhagen, 1993].
9. A. N. Strahler, A. H. Strahler, Physical Geography:Science and Systems of the Human Environment(Wiley, New York, 1997).
10. R. M. Thomas, in A Rehabilitated Estuarine Ecosystem:The Environment and Ecology of the Thames Estuary,
M. J. Attrill, Ed. (Kluwer, Dordrecht, Netherlands, 1998),pp. 115–139.
11. S. Jennings et al., Fish. Res. 40, 125 (1999).12. ICES Advisory Committee on Fishery Management
(ACFM), Report of the Northern Pelagic and BlueWhiting Fisheries Working Group (ICES CM 2004/ACFM:24, ICES, Copenhagen, 2004).
13. S. I. Rogers, J. R. Ellis, Int. Counc. Explor. Sea J. Mar.Sci. 57, 866 (2000).
14. N. Daan, H. Gislason, J. Pope, J. Rice, Changes in theNorth Sea Fish Community: Evidence of Indirect Effectsof Fishing? (ICES CM N:10, ICES, Copenhagen, 2003).
15. U.K. Climate Impacts Programme, UKCIP02 ScenariosGateway—Maps Gateway; available at www.ukcip.org.uk/scenarios/marine/marine.html.
16. N. K. Dulvy, Y. Sadovy, J. D. Reynolds, Fish Fish. 4, 25(2003).
17. S. Jennings, J. D. Reynolds, S. C. Mills, Proc. R. Soc.London Ser. B 265, 333 (1998).
18. J. D. Reynolds, S. Jennings, N. K. Dulvy, in Conserva-tion of Exploited Species, J. D. Reynolds, G. M. Mace,K. H. Redford, J. G. Robinson, Eds. (Cambridge Univ.Press, Cambridge, 2001), pp. 145–168.
19. M. Edwards, A. J. Richardson, Nature 430, 881 (2004).20. S. A. Murawski, Trans. Am. Fish. Soc. 122, 647 (1993).21. We thank A. Taylor, P. C. Reid, T. Osborn, P. Jones,
the ICES Oceanographic Database, and the UK Cli-mate Impacts Programme for providing data; J. Gill,W. Sutherland, and A. Watkinson for commenting onearlier drafts; and M. Attrill, K. Brander, R. Clark, C.Fox, J. Gill, and S. Jennings for valuable discussion.This research was undertaken under the Defra-fundedproject MF0730, with additional support from a Com-monwealth Scholarship to A.L.P. and an NERC grantto J.D.R.
Supporting Online Materialwww.sciencemag.org/cgi/content/full/1111322/DC1Materials and MethodsTables S1 to S4References
22 February 2005; accepted 28 April 2005Published online 12 May 2005;10.1126/science.1111322Include this information when citing this paper.
Community Proteomics of aNatural Microbial Biofilm
Rachna J. Ram,1 Nathan C. VerBerkmoes,3,4 Michael P. Thelen,1,6
Gene W. Tyson,1 Brett J. Baker,2 Robert C. Blake II,7
Manesh Shah,5 Robert L. Hettich,4 Jillian F. Banfield1,2*
Using genomic and mass spectrometry–based proteomic methods, we eval-uated gene expression, identified key activities, and examined partitioning ofmetabolic functions in a natural acid mine drainage (AMD) microbial biofilmcommunity. We detected 2033 proteins from the five most abundant species inthe biofilm, including 48% of the predicted proteins from the dominant biofilmorganism, Leptospirillum group II. Proteins involved in protein refolding andresponse to oxidative stress appeared to be highly expressed, which suggeststhat damage to biomolecules is a key challenge for survival. We validated andestimated the relative abundance and cellular localization of 357 unique and215 conserved novel proteins and determined that one abundant novel pro-tein is a cytochrome central to iron oxidation and AMD formation.
Microbial communities play key roles in the
Earth_s biogeochemical cycles. Our knowledge
of the structure and activities in these communi-
ties is limited, because analyses of microbial
physiology and genetics have been largely con-
fined to studies of organisms from the few lin-
eages for which cultivation conditions have been
determined (1). An additional limitation of pure
culture–based studies is that potentially critical
community and environmental interactions are
not sampled. Recent acquisition of genomic
data directly from natural samples has begun to
reveal the gene content of communities (2) and
environments (3). Here we combined Bshotgun[mass spectrometry (MS)–based proteomics
(4–6) with community genomic analysis to
evaluate in situ microbial activity of a low-
complexity natural microbial biofilm.
The biofilm samples used in this study and
prior work (2) were collected from under-
ground sites in the Richmond mine at Iron
Mountain, near Redding, California (USA).
These pink biofilms grew on the surface of
sulfuric acid–rich (pH È0.8), È42-C solu-
tions that contain near-molar concentrations
of Fe and millimolar concentrations of Zn,
Cu, and As (7) (Fig. 1). We used oligo-
nucleotide probes (8) to demonstrate that
Leptospirillum group II dominated the sample,
but it also contained Leptospirillum group III,
Sulfobacillus, and Archaea related to Ferro-
plasma acidarmanus and BG-plasma[ (Fig. 2).
This was similar in structure and composition
to the community previously used as a source
of genomic sequence (2).
In general, proteins could be assigned to
organisms, because the genes that encode them
are on DNA fragments (scaffolds) that have
been assigned to different organism types (2).
From the genomic dataset (2), we created a
database of 12,148 proteins (Biofilm_db1)
that was used to identify two-dimensional
(2D) nano–liquid chromatography (nano-LC)
(200 to 300 nl/min) tandem mass spectrome-
try (MS/MS) spectra (8–13) from different
biofilm fractions. One or more peptides were
assigned to È5994 proteins (Table 1). This
corresponds to È49% of all proteins encoded
by the genomes of the five most abundant or-
ganisms. We estimated the likelihood of false-
positive protein identification using a variety
of detection criteria and databases derived
from organisms not present in this environ-
ment (8). Because of these results, for all
subsequent analyses, we required matching of
two or more peptides per protein for confident
detection (8). After removal of duplicates, we
detected 2003 different proteins (table S1). An
additional 30 proteins were found that were
encoded by alternative or overlapping open
reading frames (8).
We detected 48% of the predicted proteins
(i.e., 1362 of 2862) from Leptospirillum group
II (table S2). This percentage exceeded those
of most prior proteomic studies of microbial
isolates (10, 12, 13). In part, this may reflect
the presence of cells in many different growth
stages, as well as microniches within the
biofilm (14). We also detected 270 Leptospi-
rillum group III, 84 Ferroplasma type I, 99
Ferroplasma type II, and 122 BG-plasma[proteins. In addition, we found 30 proteins on
unassigned archaeal scaffolds and 36 on un-
assigned bacterial scaffolds. The proportion of
proteins detected from each organism type
was similar to the proportion of cells from
each organism type in the biofilm (8). Most
proteins from low-abundance members were
probably in concentrations too low to be
detected by the presence of two peptides.
1Department of Environmental Science, Policy, andManagement, 2Department of Earth and PlanetaryScience, University of California at Berkeley, Berkeley,CA 94720, USA. 3Graduate School of GenomeScience and Technology University of Tennessee–Oak Ridge National Laboratory, 1060 CommercePark, Oak Ridge, TN 37830, USA. 4Chemical SciencesDivision, 5Life Sciences Division, Oak Ridge NationalLaboratory, Oak Ridge, TN 37831, USA. 6BiosciencesDirectorate, Lawrence Livermore National Laborato-ry, Livermore, CA 94551, USA. 7College of Pharmacy,Xavier University, New Orleans, LA, 70125, USA.
*To whom correspondence should be addressed.E-mail: [email protected]
R E P O R T S
www.sciencemag.org SCIENCE VOL 308 24 JUNE 2005 1915
1
Supporting Online Material Materials and Methods Data
Annual data describing demersal fish distribution and abundance in the North
Sea (between 51-62° latitude) from 1977-2001 were taken from the English Groundfish
Survey (EGFS) programme. Species were included in analyses if they were caught at a
minimum of five stations each year, during at least ten years. Blue whiting
(Micromesistius poutassou), although not considered a demersal fish, was retained in
the dataset because the EGFS is considered to sample this species effectively in the
North Sea. In accordance with EGFS records, redfishes (Sebastes spp.) were combined,
as were argentines (Argentina silus and A. sphyraena). Abundance-weighted mean
annual latitudes and depths were derived for each species. For fishes with northern or
southern range limits in the North Sea, abundance-weighted annual boundary latitudes
were also calculated, based on the three most extreme stations of occurrence in each
year.
Bottom temperature data were obtained from the International Council for the
Exploration of the Sea, for 1° x 1° boxes in the North Sea. Annual means and five-year
running means (to allow for potential lagged effects) were calculated for winter (the
four coldest months, December-March) and summer (the four warmest months, June-
September). Data on the winter North Atlantic Oscillation Index (NAOI) (the
normalised sea level pressure difference between Gibraltar and Reykjavik, Iceland),
which is associated with changes in fish abundance, growth, and productivity (S1, S2)
were from Jones et al. (S3), with updated values provided by the Climatic Research
Unit, University of East Anglia (S4). Gulf Stream Index (GSI) data (measuring the
relative extent of the northern wall of the Gulf Stream along the east coast of North
2
America) were obtained from the Plymouth Marine Laboratory (S5). Average annual
abundances of the calanoid copepod species Calanus helgolandicus and C.
finmarchicus (which are major food sources for many juvenile fish) (S6), were provided
by the Sir Alister Hardy Foundation for Ocean Science.
Life history parameter estimates were compiled from the primary literature,
FishBase (S7), and regional fish guides (S8, S9). Growth rates and maximum lengths
were described as K and L∞ from the von Bertalanffy growth equation,
(where L)1( )( 0ttKt eLL −−
∞ −= t is length at age t, L∞ is the asymptotic length at which
growth rate is theoretically zero, K is the rate of growth towards this asymptote, and t0 is
the age at which length would theoretically have been zero (S10)). Maximum observed
length (Lmax) was included, because it was available for more species than L∞, and
serves as a useful surrogate for L∞, (S11). We also used mean age and length at
maturity (Tmat, and Lmat) for 50% of the population. For parameter estimates and
references, see Table S4. Values were used for North Sea populations where possible,
and otherwise were selected for the closest available region. Means were calculated
where sex-specific values were reported or multiple estimates were available, and mean
values were also used for the combined redfish and argentine species.
Statistical analyses
In order to assess the direct effects of warming on species distributions, winter
and summer temperatures (same-year and five-year running means) and abundance
were entered into multiple regressions for each species, using (a) mean latitude (b)
mean depth and, where applicable, (c) boundary latitude as dependent variables. Where
significant models included both abundance and environmental predictors, semi-partial
correlation coefficients were examined to determine the relative influence of warming.
3
We also examined less direct effects of warming on distributions, through
multiple regressions in which we again controlled for changes in abundance and
regressed the distribution variables against composite variables of warming rather than
temperatures. These new variables were the first and second factors from a Principal
Components Analysis of eight environmental variables that generally described
warming (winter temperature, five-year running mean winter temperature, summer
temperature, five-year running mean summer temperature, NAOI, NAOI with a 1-year
lag, GSI, and the ratio of C. helgolandicus abundance: C. finmarchicus abundance) and
were correlated with the position of species centres and boundaries. The first principal
component (PC1) explained 47% of the variance in these variables, and generally
described warming. PC2 accounted for a further 18% of the variance. Finally, we used
simple linear regression to examine changes in mean latitudes and boundary latitudes
over time.
We tested for life history differences between species whose mean latitudes
shifted and did not shift in relation to warming. Small sample sizes limited the use of
phylogenetically paired comparisons, although life history differences lay in the
expected direction for 6 of 9 pairs.
The effects of using mean values for life history parameters estimates for
redfishes and argentines were checked by redoing analyses using values for individual
species from each group, and by excluding these groups altogether. None of these
variations altered our findings.
4
Tabl
e S1
Sta
tistic
ally
sig
nific
ant m
ultip
le re
gres
sion
s of
the
effe
cts
of 3
mea
sure
s of
Nor
th S
ea w
arm
ing
on m
ean
dept
hs o
f 36
dem
ersa
l fis
hes,
197
7-20
01.
PC
1, fi
rst P
rinci
pal C
ompo
nent
from
PC
A o
f eig
ht e
nviro
nmen
tal v
aria
bles
(PC
1 ge
nera
lly d
escr
ibes
war
min
g).
Win
ter a
nd s
umm
er te
mp.
, 5-y
ear r
unni
ng m
ean
botto
m te
mpe
ratu
res
for D
ec-M
ar a
nd J
un-S
ep, e
xcep
t whe
re a in
dica
tes
a re
latio
nshi
p w
ith a
nnua
l mea
n su
mm
er o
r win
ter t
empe
ratu
re. W
here
abu
ndan
ce
was
als
o a
sign
ifica
nt p
redi
ctor
of d
istri
bion
, s den
otes
that
r2 and
P d
escr
ibe
the
squa
red
sem
i-par
tial c
orre
latio
n co
effic
ient
(to
iden
tify
the
prop
ortio
n of
var
ianc
e
in d
istri
butio
n ac
coun
ted
for b
y w
arm
ing)
.
Spec
ies
Com
mon
nam
e df
M
ean
dept
h (m
) SD
PC1
r2 P
Win
ter
tem
p.
r2 P
Sum
mer
te
mp.
r2
P
Ago
nus
cata
phra
ctus
Po
gge
22
46.7
6.
6 A
narh
ichu
s lu
pus
Atla
ntic
wol
ffish
21
103.
0 8.
0 A
rgen
tina
spp.
Arge
ntin
es24
13
8.1
7.
6
A
rnog
loss
us la
tern
a Sc
aldf
ish
1535
.5
4.3
4.
153a
0.34
0.01
7B
uglo
ssid
ium
lute
um
Sole
nette
22
33.8
3.9
2.17
00.
320.
005
2.76
5a 0.
250.
012
5.46
10.
280.
007
Cal
liony
mus
lyra
D
rago
net
23
55.7
12.4
6.88
20.
310.
005
17.7
68
0.35
0.00
213
.973
0.16
0.04
9E
chiic
hthy
s vi
pera
Le
sser
wee
ver
24
33.2
2.
9
3.30
00.
18
0.03
4E
utrig
la g
urna
rdus
G
rey
gurn
ard
2362
.66.
54.
223
0.42
0.00
110
.534
0.47
<0.0
0110
.809
0.
360.
001
Gad
icul
us a
rgen
teus
Si
lver
y po
ut
23
160.
1 9.
1
Gad
us m
orhu
a At
lant
ic c
od
23
81
.77.
95.
5024
0.
33s
<0.0
019.
692
0.
22s
0.00
113
.465
0.
31s
<0.0
01
Gly
ptoc
epha
lus
cyno
glos
sus
Witc
h24
12
0.5
10.3
Hip
pogl
osso
ides
pla
tess
oide
s Lo
ng ro
ugh
dab
23
97.5
4.
4
2.29
3
0.28
0.
009
5.95
1
0.35
0.00
26.
014
0.
270.
008
Lepi
dorh
ombu
s bo
scii
Four
spot
meg
rim24
14
8.3
9.8
Leuc
oraj
a na
evus
C
ucko
o ra
y 19
81
.2
14.6
19.9
860.
360.
005
Lim
anda
lim
anda
D
ab23
58.8
3.5
2.03
30.
340.
003
3.62
10.
200.
024
5.81
1
0.39
0.
001
Lo
phiu
s pi
scat
oriu
s An
gler
fish
23
99.4
10
.5
5.
841
0.28
0.00
9
14.0
490.
340.
002
Lum
penu
s la
mpr
etae
form
is
Snak
e bl
enny
12
10
1.1
21.3
61
.100
0.
87<0
.001
M
elan
ogra
mm
us a
egle
finus
H
addo
ck24
98
.53.
53.
999
0.
240.
013
Mer
lang
ius
mer
lang
us
Whi
ting
2476
.4
4.5
Mer
lucc
ius
mer
lucc
ius
Hak
e24
12
1.1
13.8
Mic
rom
esis
tius
pout
asso
u Bl
ue w
hitin
g 20
16
5.0
14.0
7.
889
0.33
0.00
712
.645
a 0.
400.
002
Mic
rost
omus
kitt
Le
mon
sol
e 24
80
.7
4.5
Mol
va m
olva
Li
ng24
129.
316
.3
Myx
ine
glut
inos
a H
agfis
h11
11
3.5
12.6
Ple
uron
ecte
s pl
ates
sa
Plai
ce23
53
.13.
5 1.
92
0.
31
0.00
53.
348
0.
170.
041
5.42
7
0.33
0.00
3P
olla
chiu
s vi
rens
Sa
ithe
24
143.
37.
1
Pse
tta m
axim
a Tu
rbot
13
43.7
6.6
Rhi
none
mus
cim
briu
s Fo
ur-b
eard
ed ro
cklin
g 22
91
.4
13.1
6.05
8
0.22
0.
024
16.7
90
0.31
0.00
5S
cylio
rhin
us c
anic
ula
Sm
all-s
potte
d ca
tsha
rk
20
83.8
9.
9 S
ebas
tes
spp.
R
edfis
h18
13
7.9
17
.1
8.
201
0.
27
0.02
415
.044
a 0.
390.
004
Sol
ea s
olea
C
omm
on s
ole
1337
.5
6.7
S
qual
us a
cant
hias
Sp
urdo
g19
69
.811
.8
Trig
la lu
cern
a Tu
b gu
rnar
d 19
33
.12
2.3
Tr
isop
teru
s es
mar
kii
Nor
way
pou
t 23
11
8.0
4.3
-1
.922
0.
200.
029
Tr
isop
teru
s lu
scus
Bi
b9
39.5
6.
5
Tr
isop
teru
s m
inut
us
Poor
cod
23
67
.1
8.6
4.12
60.
230.
018
11.2
460.
330.
003
5
Tabl
e S2
Sta
tistic
ally
sig
nific
ant m
ultip
le re
gres
sion
s of
the
effe
cts
of 3
mea
sure
s of
Nor
th S
ea w
arm
ing
on b
ound
ary
latit
udes
of 2
0 de
mer
sal
fishe
s, 1
977-
2001
. P
C1,
firs
t Prin
cipa
l Com
pone
nt fr
om P
CA
of 8
env
ironm
enta
l var
iabl
es (P
C1
gene
rally
des
crib
es w
arm
ing)
. W
inte
r and
sum
mer
tem
p., 5
-yea
r run
ning
mea
n bo
ttom
tem
pera
ture
s fo
r Dec
-Mar
and
Jun
-Sep
, exc
ept w
here
a indi
cate
s a
rela
tions
hip
with
ann
ual m
ean
sum
mer
or
win
ter t
empe
ratu
re.
Whe
re a
bund
ance
was
als
o a
sign
ifica
nt p
redi
ctor
of d
istri
butio
n, s d
enot
es th
at r2 a
nd P
des
crib
e th
e sq
uare
d se
mi-p
artia
l
corr
elat
ion
coef
ficie
nt (t
o id
entif
y th
e pr
opor
tion
of v
aria
nce
in d
istri
butio
n ac
coun
ted
for b
y w
arm
ing)
.
S
peci
es
Com
mon
nam
e df
Bou
ndar
y la
titud
e (°
N)
SD
PC
1r2
P
W
inte
r te
mp.
r2
P
S
umm
er
tem
p.
r2 P
Nor
ther
ly s
peci
es
A
narh
ichu
s lu
pus
Atla
ntic
wol
ffish
2156
.18
0.77
Gad
icul
us a
rgen
teus
Si
lver
y po
ut
2358
.50
0.59
Gly
ptoc
epha
lus
cyno
glos
sus
Witc
h23
55.7
00.
740.
293
0.15
s 0.
012
0.70
60.
17s
0.01
0.89
3
0.18
s
0.
007
H
ippo
glos
soid
es p
late
ssoi
des
Long
roug
h da
b 23
54
.11
0.28
0.
138
0.
24 0.
016
0.
545
0.
60s
<0.0
01 Le
pido
rhom
bus
bosc
ii Fo
ursp
ot m
egrim
24
59.0
11.
00Le
ucor
aja
naev
us
Cuc
koo
ray
1956
.80
0.38
Mel
anog
ram
mus
aeg
lefin
us
Had
dock
2353
.96
0.49
0.26
70.
28s
0.00
60.
379a
0.29
0.00
8M
icro
mes
istiu
s po
utas
sou
Blue
whi
ting
20
57.6
71.
720.
923
0.
29 0.
011
1.
452a
0.34
0.00
5
Mol
va m
olva
Li
ng23
56.4
11.
00P
olla
chiu
s vi
rens
Sa
ithe
2456
.89
0.88
Seb
aste
s sp
p.
Red
fish
1858
.46
0.83
1.39
1
0.38
0.00
5
Tris
opte
rus
esm
arki
i N
orw
ay p
out
24
54.8
50.
51
0.35
9a 0.
230.
015
Sou
ther
ly s
peci
esA
gonu
s ca
taph
ract
us
Pogg
e22
57.4
71.
07
Arn
oglo
ssus
late
rna
Scal
dfis
h15
54.9
10.
610.
542
0.
11
0.02
9
Bug
loss
idiu
m lu
teum
So
lene
tte23
55.2
50.
53E
chiic
hthy
s vi
pera
Le
sser
wee
ver
23
54.7
00.
370.
162
0.
19 0.
035
0.
500
0.
30 0.
004
P
setta
max
ima
Turb
ot13
55.9
70.
92S
olea
sol
ea
Com
mon
sol
e13
54.9
10.
78Tr
igla
luce
rna
Tub
gurn
ard
19
55.2
30.
85
-1.0
84
0.24
0.02
9Tr
isop
teru
s lu
scus
Bi
b8
54.2
30.
730.
549
0.88
<0.0
010.
997
0.55
0.01
52.
026
0.82
<0.0
01
6
Tabl
e S3
Sta
tistic
ally
sig
nific
ant l
inea
r reg
ress
ions
bet
wee
n tim
e an
d (i)
bou
ndar
y la
titud
es; a
nd (i
i) m
ean
latit
udes
of N
orth
Sea
dem
ersa
l fis
hes,
197
7-
2001
. *La
titud
e sh
ifted
sou
th w
ith w
arm
ing.
Arr
ows
indi
cate
whe
re a
bund
ance
has
incr
ease
d or
dec
reas
ed s
igni
fican
tly o
ver t
ime.
See
Tab
le 1
and
Tab
le
S2
for f
ull s
peci
es li
sts
and
rela
tions
hips
bet
wee
n di
strib
utio
ns a
nd w
arm
ing.
Spe
cies
Com
mon
nam
e df
La
titud
e (°
N)
SD
Y
ear
coef
ficie
nt
r2 P
La
titud
e re
late
d to
war
min
g?
Abu
ndan
ce
trend
ove
r tim
e (i)
Bou
ndar
y La
titud
es
N
orth
erly
spe
cies
Ana
rhic
has
lupu
s A
tlant
ic w
olffi
sh
2156
.18
0.77
0.
079
0.48
<0
.001
No
↓ G
lypt
ocep
halu
s cy
nogl
ossu
s W
itch
2455
.73
0.73
0.06
80.
46
<0.0
01Y
esLe
pido
rhom
bus
bosc
ii Fo
ursp
ot m
egrim
24
59.0
1 1.
000.
076
0.32
0.00
3N
oM
icro
mes
istiu
s po
utas
sou
Blu
e w
hitin
g 21
57.7
1 1.
69
0.11
1 0.
27
0.01
4Y
es
M
olva
mol
va
Ling
2356
.41
1.00
0.06
80.
24
0.01
5N
oTr
isop
teru
s es
mar
kii
Nor
way
pou
t
2454
.85
0.51
0.04
1
0.35
0.00
2Y
es
↑
Sou
ther
ly s
peci
es
A
rnog
loss
us la
tern
a S
cald
fish
1554
.91
0.61
0.
052
0.32
0.
022
Yes
Ech
iicht
hys
vipe
ra
Less
er w
eeve
r 24
54.7
2 0.
39
0.03
5 0.
43
<0.0
01Y
es
↑ Tr
isop
teru
s lu
scus
B
ib9
54.4
30.
940.
093
0.
80
0.00
1Y
es (ii
) M
ean
latit
udes
Ago
nus
cata
phra
ctus
P
ogge
2254
.67
0.90
-0
.074
0.37
0.
002
No
Cal
liony
mus
lyra
D
rago
net
2455
.44
0.69
0.
063
0.45
<0
.001
Yes
Eut
rigla
gur
nard
us
Gre
y gu
rnar
d 24
56.1
5 0.
36
0.03
6 0.
54
<0.0
01Y
es
↑ G
adus
mor
hua
Atla
ntic
cod
24
56.8
4 0.
36
0.03
5 0.
53
<0.0
01Y
es
↓ Le
pido
rhom
bus
bosc
ii Fo
ursp
ot m
egrim
24
60.5
1 0.
370.
021
0.17
0.04
1N
oLu
mpe
nus
lam
pret
aefo
rmis
S
nake
ble
nny
1256
.52
1.15
0.
107
0.63
0.
001
Yes
Rhi
none
mus
cim
briu
s Fo
ur-b
eard
ed ro
cklin
g 24
56.0
70.
670.
055
0.36
0.00
2Y
esS
cylio
rhin
us c
anic
ula
Sm
all-s
potte
d ca
tsha
rk
2058
.34
0.89
-0
.077
0.
35
0.00
4N
o ↑
Sol
ea s
olea
C
omm
on s
ole
1353
.72
0.66
-0
.049
0.
36
0.02
4Y
es*
Tr
isop
teru
s es
mar
kii
Nor
way
pou
t 24
58.5
9 0.
26
-0.0
19
0.29
0.
005
Yes
* ↑
7
Table S4 Life history parameter estimates for 36 North Sea demersal fish species. L∞,
asymptotic (maximum) length; K, growth rate from the von Bertalanffy equation; Lmax, maximum
recorded length; Tmat, age at which 50% of the population are mature; Lmat, length at which 50%
of the population are mature.
Species Common Name L∞ (cm)
Lmax (cm)
K (y-1)
Tmat (y)
Lmat (cm)
References
Agonus cataphractus Pogge 15 20 0.475 . . S8, S12, S13 Anarhichas lupus Atlantic wolffish 162.5 125 0.0435 6.5 55 S8, S14 Arnoglossus laterna Scaldfish 15 25 0.936 . 6.8 S8, S15, S16 Buglossidium luteum Solenette 10.75 12.5 0.573 3 7 S8, S15, S17 Callionymus lyra Dragonet 23 25 0.47 2.5 13 S8, S18 Echiichthys vipera Lesser weever . 17 . . . S19 Eutrigla gurnardus Grey gurnard 46 45 0.16 3 23 S8, S20 Gadiculus argenteus Silvery pout 15.5 15 0.693 . . S8, S21 Gadus morhua Atlantic cod 123.1 131.8 0.23 3.8 69.7 S11, S22 Glyptocephalus cynoglossus Witch 44 60 0.2 4.5 29 S20 Hippoglossoides platessoides Long rough dab 25 30 0.34 2.6 15 S7, S20 Lepidorhombus boscii Fourspot megrim 50 35 0.155 1.5 . S7, S23, S24 Leucoraja naevus Cuckoo ray 92 70 0.11 9 59 S8, S20 Limanda limanda Dab 27 40 0.26 2.3 13 S8, S20 Lophius piscatorius Anglerfish 106 74.6 0.18 4.5 61 S11, S20 Lumpenus lampretaeformis Snake blenny . 50 . 3 20 S8 Melanogrammus aeglefinus Haddock 68.3 75.5 0.19 2.5 33.5 S22 Merlangius merlangus Whiting 42.4 44.9 0.32 1.5 20.2 S11, S22 Merluccius merluccius Hake 105 135 0.184 6.75 51.75 S8, S25 Micromesistius poutassou Blue whiting 37.1 34 0.23 2.3 25.1 S7, S22 Microstomus kitt Lemon sole 37 60 0.42 4 27 S8, S20, S26 Molva molva Ling 183 200 0.118 6.5 90 S8, S27 Myxine glutinosa Hagfish . 80 . . 25 S8 Pleuronectes platessa Plaice 54.5 95 0.11 2.5 26.6 S8, S22 Pollachius virens Saithe 177.1 130 0.07 4.6 55.4 S8, S22 Psetta maxima Turbot 69.6 100 0.2497 4.5 49 S8, S15, S17, S28 Rhinonemus cimbrius Four-bearded rockling 36 40 0.2 3 25 S8, S20 Scyliorhinus canicula Small-spotted catshark 88 100 0.2 5 58 S8, S20 Sebastes marinus Redfish 74.4 100 0.0615 11 35 S8, S29 Sebastes viviparus Norway haddock 36 35 0.07 20 12.5 S8, S20 Solea solea Common sole 45.3 60 0.363 4 29 S8, S15 Squalus acanthias Spurdog 90 105 0.15 6.5 67 S8, S20 Trigla lucerna Tub gurnard 68.15 75 0.1524 . . S8, S30, S31 Trisopterus esmarkii Norway pout 23 25 0.52 2.3 19 S8, S22 Trisopterus luscus Bib 42.35 46 0.211 2 22.5 S17, S32 Trisopterus minutus Poor cod 20 40 0.51 2 15 S8, S20
8
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