Ecosystems (2014) 17: 550-566
DOI: 10.1007/s 10021-013-9742-4
ECOSYSTEMS!
) 2014 The A uthor(s). This article is published w ith open access at Springerlink.com
Large-Scale Spatial Dynamics of
Intertidal Mussel (Mytilus edulis L.)
Bed Coverage in the German
and Dutch Wadden Sea
Eelke O. Folm er,1* Jan D rent,1 Karin Troost,2 Heike Büttger,3
Norbert D ankers,4 Jeroen Jansen,4 M arnix van Stralen,5 Gerald M illat,6
Mare H erlyn,7 and Catharina J. M. P hilippart1
1Department of Marine Ecology, Royal Netherlands Institute for Sea Research (NIOZ), P.O. Rox 59, 1790 AE Den Eurg, Texel, The
Netherlands; 2Institute for Marine Resources & Ecosystem Studies, Yerseke, The Netherlands; 3EioConsult SH, Schobüller Str. 36,
25813 Husum, Germany; 4Institute for Marine Resources Er Ecosystem Studies, Texel, The Netherlands; 5MarinX, Elkerzeeseweg 77,
4322NA Scharendij'ke, The Netherlands; 6National Park Administration Wadden Sea Lower Saxony, Virchowstr. 1, 26340 Wil­
helmshaven, Germany; 7Niedersächsischer Landesbetrieb für Wasserwirtschaft, Küsten- und Naturschutz, An der Mühle 5, D-26548
Norderney, Germany
A
bstr a c t
Intertidal blue m ussel beds are im portant for the
functioning and com m unity com position of
coastal ecosystems. M odeling spatial dynam ics of
intertidal m ussel beds is com plicated because
suitable habitat is spatially heterogeneously dis­
tributed and recruitm ent and loss are hard to
predict. To get insight into the m ain determ inants
of dispersion, grow th and loss of intertidal m ussel
beds, we analyzed spatial distributions and
grow th patterns in the G erm an and D utch W ad­
den Sea. We considered yearly distributions of
adult intertidal m ussel beds from 36 connected
tidal basins betw een 1999 and 2010 and for the
period 1968-1976. We found th at in both periods
the highest coverage of tidal flats by m ussel beds
occurs in the sheltered basins in the sou th ern
W adden Sea. We used a stochastic grow th m odel
to investigate the effects of density dependence,
w inter tem perature and storm iness on changes in
m ussel bed coverage betw een 1999 and 2010. In
contrast to expectation, we found no evidence
th at cold w inters consistently induced events of
synchronous population grow th, no r did we find
strong evidence for increased rem oval of adult
m ussel beds after storm y w inter seasons. H ow ­
ever, we did find synchronic grow th w ithin
groups of proxim ate tidal basins and th at syn­
chrony betw een distant groups is m ainly low or
negative. Because the boundaries betw een syn­
chronic groups are located n ear river m ouths and
in areas lacking suitable m ussel bed habitat, we
suggest th at the m etapopulation is u n d er the
control of larval dispersal conditions. O ur study
dem onstrates the im portance of m oving from
simple habitat suitability m odels to m odels
th at incorporate m etapopulation processes to
R eceived 27 M ay 2013; accepted 27 N ovem ber 2013;
published online 7 J a n u a ry 2014
E le ctro n ic s u p p le m e n ta r y m a teria l: The online version of this article
(doi:10 .1 0 0 7 /sl0 0 2 1 -0 1 3 -9 7 4 2 -4 ) contains su p p lem en tary m aterial,
w h ich is available to a u th o rized users.
A u th o r C o n trib u tio n s: EOF, JD, ND, MvS, HB an d CJMP conceived
an d designed th e study. C arried o u t in tertid al m ussel bed m o n ito ring and
prep ared th e GIS data: KT, ND, JJ, MvS (N etherlands); GM an d MH
(Lower Saxony); HB (Schlesw ig-H olstein). EOF com piled th e data sets
an d analyzed th e data. EOF w rote th e p ap er to w h ich all co-authors
contributed.
*Corresponding author; e-mail: E.0.Folm [email protected] gm ail.com
550
Spatial Dynamics of Intertidal M ussel Beds
551
u n d erstan d spatial dynam ics of m ussel beds. The
spatio-dynam ic structure revealed in this paper
will be in stru m ental for th at purpose.
K ey w ords: population synchronization;
tivity; supply side ecology; larvae; habitat
ity; spatial autocorrelation; spatial
dynam ic ecosystem; M ytilus edulis ; Getis
tistic.
connec­
suitabil­
stability;
Ord sta­
I n t r o d u c t io n
strategy th at adjusts spaw ning according to envi­
ronm ental conditions (Gosling 1992b). Larvae and
settling individuals are observed in spring, sum m er,
and au tu m n (Bayne 1976; Pulfrich 1996; de Vooys
1999; Philippart an d others 2012). Dispersal of the
pelagic planktotrophic m ussel larvae is strongly
determ ined by w ater currents (Levin 2006; Metaxas an d Saunders 2009; Carson and others 2010;
W hite and others 2010) an d the duration of the
planktonic larval stage w hich depends on envi­
ronm ental factors such as food availability, tem ­
perature, and salinity (Gosling 1992b). Blue m ussel
larvae have been estim ated to reside in the w ater
colum n for approxim ately 3-5 weeks (Gosling
1992b; de Vooys 1999). W hen larvae reach a size of
approxim ately 0.2 m m they settle and produce
byssus threads to attach to substrata and to each
other w hich protects th em from dislodgem ent due
to currents an d w aves (Bayne 1964, 1976; De Blok
and Tan-M aas 1977; M cGrath and others 1988).
Im portant substrata on the intertidal soft sedim ents
in the W adden Sea are adult mussels, Pacific oys­
ters (Crassostrea gigas), cockles (Cerastoderma edule),
beds of dead shells, protruding tubes of Lanice
conchilega (Callaway 2003), m acroalgae and seagrass (Pulfrich 1996; w a Kangeri and others 2013).
Bed form ation results in increased bottom surface
roughness w hich reduces hydrodynam ic force
(W iddows an d Brinsley 2002; D onker an d others
2012). Intertidal m ussel beds th at have established
on soft sedim ents provide substratum for attach ­
m en t an d shelter of postlarvae w hich results in
positive feedback betw een m ussel bed presence and
local recruitm ent allow ing a bed to persist over
long tim e spans (Bayne 1964; M cGrath and others
1988; Gutiérrez and others 2003; Herlyn and others
2008). Foundations of dead shells w hich are built
up u n d ern eath m ussel beds m ay also provide sub­
stratum for postlarvae thereby upholding th e p o ­
sitive feedback even w h e n living m ussels have
disappeared (Herlyn an d others 2008). Settlem ent
m ay, how ever, also occur outside m ussel beds or
shell foundations in years w h e n there are large
quantities of settlers. Predation by shrim p and crabs
(van der Veer and others 1998; A ndresen and van
der M eer 2010) is an im portant loss factor shortly
after settlem ent w hile w aves and currents (Nehls
Reef-building bivalves such as intertidal blue
m ussel (M ytilus edulis) are im portant for the com ­
m u n ity com position and functioning of coastal
ecosystems for various reasons. Intertidal m ussel
beds provide structural heterogeneity on otherw ise
hom ogeneous m udflats and thus provide habitat
for other species (Gutiérrez an d others 2003;
B uschbaum an d others 2009; van der Zee and
others 2012). Mussels m ay com pete w ith other
suspension feeders by consum ing large am ounts of
ph y to plankton (Kam erm ans 1994; Cadée and
Hegem an 2002). C onsequently, secondary p ro ­
duction and biom ass of intertidal m ussel beds is
high (Asrnus 1987) m aking th em an im portant
food resource for, am ongst others, birds and crabs
(Nehls and others 1997). M ussels m ay also prom ote
planktonic prim ary production by accelerating
n u trie n t cycling (Asrnus an d Asrnus 1991) and lo­
cal b enthic prim ary production by deposition of
n u trien t-rich feces an d pseudofeces. Lastly, m ussel
beds m ay locally reduce hydrodynam ic forces
w hich influences local sedim ent properties (Widdows and Brinsley 2002). To develop a better
u n derstanding of th e roles th at intertidal m ussel
beds play in coastal ecosystems, it is im portant to
identify th e m ain determ inants of m ussel bed dis­
tributions an d dynam ics. Insight into spatial dis­
tributions,
h abitat
suitability,
reproduction,
dispersal, settlem ent, and survival are prerequisites
to u n d erstan d an d m odel large-scale spatio-tem ­
poral variability of m arine bivalve populations. The
purpose of th e present paper is to foster our
u n derstanding
of long-term
spatio-tem poral
developm ent of adult intertidal m ussel bed distri­
butions in th e W adden Sea as a step tow ards the
developm ent of spatial nretapopulation m odels of
blue mussels.
Spatial distributions and dynam ics of intertidal
m ussel beds are determ ined by th e com bination of
larval supply, settlem ent an d various post-settle rnent loss processes w hich in their tu rn depend on
th e interplay of large-scale hydrodynam ic forces
and small-scale facilitation (van de Koppei and
others 2005, 2012). The blue m ussel is a gonochoric bivalve species w ith a flexible reproductive
552
E. O. Folm er an d others
and Thiel 1993; B rinkm an and others 2002; H am ­
m ond an d Griffiths 2004; Herlyn and others 2008;
D onker and others 2012) and ice-scouring during
cold w inters (Strasser and others 2001) m ay be
im portant during all life stages of m ussels and
m ussel beds.
A dult bivalve population sizes m ay fluctuate vig­
orously as a consequence of high tem poral variation
in larval supply, early stage survival and variation in
adult m ortality rate (Thorson 1950; Gosling 1992a;
B eukem a and others 1993, 2001, 2010; Caley and
others 1996; van der M eer and others 2001; Levin
2006). In th e W adden Sea, Macoma balthica, Cerasto­
derma edule , Mya arenaria , and Mytilus edulis often
show recruitm ent peaks after cold w inters (Beukema
and others 2001; Strasser and others 2003). Bivalve
recruitm ent peaks following cold w inters have also
been observed in other northw estern European seas
and estuaries such as the Swedish w est coast (Möller
1986) and the W ash, UK (Young and others 1996).
Various explanations have been suggested for this.
First, adults produce m ore or larger eggs u n d er cold
conditions because lower m aintenance m etabolism is
required, thus leaving m ore energy for gonad p ro ­
duction (Honkoop and van der M eer 1998). It should
be noted, how ever, th at the experim ent th at H on­
koop and van der M eer (1998) perform ed w ith M a­
coma balthica shows th at the tem perature effect on
gonad production is rather small. Secondly, bivalve
spawning m ay occur earlier in the season after w arm
w inters w hich m ay lead to a tem poral m ism atch w ith
th e spring bloom of phytoplankton (which, inter alia ,
depends on light conditions) w hich decreases
recruitm ent (Philippart and others 2003). The third
line of explanations concerns the tem perature-re­
lated phenology of mussels and their m ain crustacean
predators, shore crab Carcinus maenas and brow n
shrim p Crangon crangon (van der Veer and others
1998; A ndresen and van der M eer 2010). Specifically,
post-settlenrent survival on intertidal flats is relatively
high after cold w inters because of low er densities or
later arrival of shrimps and crabs (Beukema 1991,
1992; Young and others 1996; Strasser and G ünther
2001). Reduced densities or later arrival of predators
leads to a larger proportion of young bivalves to grow
too large to be handled and consum ed by predators
(Philippart and others 2003; B eukem a and Dekker
2005). M ortality of adult bivalves m ay also peak
during cold w inters because frost is lethal for cockles
(Cerastoderma edule) and ice-scouring m ay eradicate
intertidal mussel beds (Strasser and others 2001;
Büttger and others 2011).
The observation th at recruitm ent peaks often
follow cold w inters suggests th at w in ter tem p era­
ture drives large-scale spatial population synchro­
nization
because
w in ter
tem peratures
are
correlated over large geographic ranges. Particu­
larly, B eukem a and others (2001) show th at
recruitm ent of th e bivalve species Macoma balthica ,
Cerastoderma edule , an d M ya arenaria is spatially
synchronized betw een three distant locations in
the w estern D utch and w estern G erm an W adden
Sea betw een 1973 and 1999 (Mytilus was n o t in ­
cluded in th e m onitoring in tw o of the three sites).
Synchronous dynam ics on sm aller spatial scales
can result from spatial autocorrelation in m ore re ­
gional grow th determ inants. For instance, diverg­
ing regional trends in food availability, predation,
and hydrodynam ic im pact could lead to distinct
synchronization patterns betw een regions. Syn­
chronous dynam ics am ong nearby populations can
also result from distance-lim ited dispersal of larvae
betw een populations (Bjornstad an d others 1999;
R anta and others 2006). The supply of planktonic
larvae in a particular region depends on th e p ro x ­
im ity of larvae sources and hydrodynam ic transport
tow ard th e region (Levin 2006) and, to a certain
extent, also on larval swim m ing behavior (Knights
and others 2006). As the sw im m ing capacity of
larvae is lim ited relative to w ater currents, the
spatial connectivity of th e m ussel populations, and
therefore th e degree of population synchrony, will
largely depend on regional geonrorphology and
currents during the larval stage (Young and others
1996; M cQuaid an d Phillips 2000; Gilg and Hilbish
2003; W atson and others 2010).
Tem perature, the spatially diverging grow th
determ inants and dispersal will lead to character­
istic signatures in spatial population covariance.
That is, the spatial range over w hich th e determ i­
nants operate, will show up in the spatial range of
population synchronization (Bjornstad an d others
1999). If tem perature is th e dom inant determ inant,
it is expected th at synchronization will be observed
in th e entire population. If regionally distinct
determ inants are im portant th e n synchronization
patterns will show autocorrelation on a regional
scale. And m ore specifically, if geonrorphology and
hydrodynam ic transport are im portant determ i­
nants of population synchronization, physical b ar­
riers such as rivers will show up as boundaries of
synchronizing populations. Because tem perature
and th e regional m echanism s operate sim ulta­
neously, the tem perature effect m ust dom inate the
regional m echanism s to achieve visible synchroni­
zation at a large spatial scale. Similarly, regional
effects m ust be pronounced in order to show up if
there are strong tem perature effects.
The objective of this study is to obtain insight
into the distribution and dynam ics of intertidal
Spatial Dynamics of Intertidal M ussel Beds
m ussel beds in the W adden Sea by exploring large
scale spatio-tem poral trends and scale-dependent
population synchronizations. First, w e com pare the
m ean spatial distribution of intertidal m ussel beds
for th e period 1999-2010 w ith the spatial distri­
b u tio n of th e period 1968-1976 to get insight into
long-term spatial variability and into the determ i­
nants of h ab itat suitability. Secondly, w e analyze
spatial patterns in the synchronization of intertidal
m ussel bed dynam ics for th e period 1999-2010.
Thirdly, w e use a stochastic grow th m odel to a n a ­
lyze th e impacts of w inter tem perature and
storm iness on intertidal m ussel bed dynam ics for
th e period 1999-2010 w hile controlling for density
dependence. The analyses presented here aim to
stim ulate th e developm ent of spatially explicit
nretapopulation m odels including habitat suitabil­
ity, resource dynam ics an d hydrodynam ic con­
nectivity. Insight into these factors is relevant from
a m an ag em en t and conservation point of view
w h ere insight into long-term variability an d n a tu ­
ral establishm ent of intertidal m ussel beds is a basic
requirem ent.
M
ethods
Study Region and Tidal Basins
The W adden Sea (52°57,-5 5 °3 7 ,N, 404 4 '-8 012'E) is
a shallow sea located in the southeastern part of the
N orth Sea bordering the coastal m ainland of D en­
m ark, Germ any, and the N etherlands (Figure 1)
[For a recent description of the biotic an d abiotic
properties of th e W adden Sea see Philippart and
Epping (2010)]. It is one of the w orld's largest
coherent systems of intertidal sand an d m ud flats
(Reise 2005; Reise and others 2010). The area
contains coastal w aters, intertidal sandbanks,
m udflats, shallow subtidal flats, drainage gullies
and deeper inlets and channels. The Elbe, W eser,
and Erns are th e m ain rivers discharging into the
W adden Sea (Figure 1) w ith average freshw ater
run-off of 700, 310, an d 125 nr3 s_1, respectively
(Philippart an d Epping 2010). Freshw ater also e n ­
ters th e W adden Sea at an average rate of approx­
im ately 470 nr3 s_1 through tw o sluices in the dike
("Afsluitdijk” ) in tidal basin 39 (van R aaphorst and
de Jonge 2004). Tidal currents and exposure to
w aves differ b etw een regions due to differences in
tidal range, geonrorphology, fetch, and the occur­
rence of barrier islands (Figure 1 ). Tidal am plitudes
range betw een 1.5 an d 3.0 nr in the N orthern (Ti­
dal Basins 1-10; henceforth TB denotes "tidal b a ­
sin") and S outhern (TB 23-39) W adden Sea and
are greater th a n 3.0 nr in the central part (TB 11-
553
22) (Staneva and others 2009). The m oderate and
storm y w inds in th e area are m ainly southw esterly.
The area can be divided into tidal basins w hich
are natu ral geonrorphological an d hydrodynam ic
subunits. A tidal basin is delineated by the m ain ­
land, barrier islands, tidal divides and it connects to
the N orth Sea via tidal inlets (Figure 1). The vol­
um e of w ater th at is being exchanged w ith the
N orth Sea through tidal inlets is m uch larger th a n
the volum e of w ater th at is displaced betw een
neighboring tidal basins (Ridderinkhof and others
1990). W e use tidal basins as units of observation
because locations w ithin tidal basins share various
morphological, hydrodynam ic, and trophic p ro p ­
erties th at are relevant for the abundance of m ussel
beds (van B eusekom and others 2012). F u rth er­
m ore, the tu rn o v er tim e of a tidal basin— defined
by R idderinkhof and others (1990) as the tim e
necessary to reduce th e mass present in a basin to a
fraction e_1 of the original mass—is relevant for the
transport of larvae betw een tidal basins. In partic­
ular, R idderinkhof and others (1990) show th at the
sm aller tidal basins in the D utch W adden Sea have
short tu rn o v er times betw een 8 and 10 tidal periods
(one tidal period is approxim ately 12.5 h) w hereas
larger tidal basins such as th e Erns (Figure 1, TB 30)
estuary have tu rn o v er times of 25 tidal periods.
Data
We used tw o data sets on m ussel bed distributions
from tw o periods. The first consists of data collected
in 36 tidal basins in the G erm an an d D utch W ad­
den Sea (Figure 1) for the period 1999-2010. The
D anish tidal basins (TB 1-3 and the N orthern part
of TB 4) w ere n o t included in this study because in
these basins an incom parable survey protocol was
applied and surveys took place every 2 years rath er
th a n every year. A nnual m apping of m ussel bed
contours in Schleswig-Holstein (TB 4-18), Ger­
m any, Lower Saxony (TB 18-30), G erm any and
the N etherlands (TB 30-39) was perform ed on the
basis of a com m on definition of a m ussel bed (de
Vlas and others 2005) an d according to trilateral
TMAG agreem ents (TMAG = Trilateral M onitoring
and Assessm ent Group 1997). TB 4 refers to the
southern half of th e tidal basin only because the
n o rth ern half is located in D enm ark an d n o t su r­
veyed in a com parable fashion. The contours of
visited m ussel beds w ere determ ined by a com bi­
n ation of aerial surveys, aerial photographs, and by
w alking around th e bed w ith a han d -h eld GPS
device following th e TMAG protocol (de Vlas and
others 2005). In Schleswig-Holstein intertidal
m ussel beds w ere m onitored by BioConsult SH
554
E. O. Folm er an d others
I
50
1 0 0 km
I
-
M u s s e l b e d c o v e r a g e (% )
Denmark
n o d a ta
I I < 0.01
I I 0.01 - 0.28
I I 0.28 - 0.59
I I 0.59 - 1.04
North Sea
■
1.04 - 1.26
■
1.26 - 1.85
■
1.85 - 2.28
■
2.28 - 2.75
■
2.75 - 3.35
■
3.35 - 6.01
SH
n o r th
Germany
s o u th
W e a th e r s ta tio n s
★
e a st
LS
w est
C uxhaven
N o rd ern ey
Germany
NL
w est
V lie la n d
H o o r n T e r s c h e llin g
E m den
L au w erso o g
F igure 1. The 39 tidal
basins of th e W adden Sea
w ith average intertidal
m ussel bed coverage over
th e years 1999-2010.
Intertidal m ussel bed
coverage is defined as th e
percentage of tidal flat
area th a t is covered by
m ussel beds. The
intertidal flats in tidal
basins 1-3 in D enm ark
are colored gray because no
com parable m ussel bed
data w ere available. The
nam es of th e w eath er
stations are printed
against a yellow
background to clearly
distinguish th em from th e
region nam es. The
geographic ex ten t ranges
b etw een 52°57'-55°37'
N orth an d 4 °4 4'-8°12'
Bast (Color figure online).
W eser
Netherlands
since 1998; in Lower Saxony by NLPV and NLWKN
since 1994 (except for 1995 an d 1998); in the
N etherlands by IMARES and M arinX since 1995.
The largest com plete an d hom ogeneous data set
th u s results from th e period 1999-2010. Region
specific survey details are described below.
In Schleswig-Holstein aerial photographs w ere
taken an n u ally (except for the years 2006 and
2009) w ith scale of 1:15.000 or 1:25.000. The
photographs w ere checked visually and the con­
tours of ascertained m ussel beds w ere digitized and
included in a GIS. Results w ere com bined w ith field
surveys. Each year about 40% of th e m ussel bed
locations w ere visited. For those locations w here
field surveys w ere no t possible, aerial photographs
provided th e only source of inform ation. For the
beds n o t visited in 2006 an d 2009 it was assum ed
th at th e contours w ere th e same as the year before.
It is noted, how ever, th at during the field cam ­
paigns in 2006 and 2009 n ew beds and the disap­
pearance of beds w ere observed.
In Lower Saxony contours of m ussel beds w ere
determ ined on the basis of black and w hite aerial
photographs w ith a scale of 1:15.000. Since 2006
true color photographs w ith a scale of 1:20.000
have been used. Photographs w ere analyzed w ith a
stereoscope an d contours of m ussel beds w ere
digitized and included in a GIS. For the year 2010
digital orthophotos w ere used and digital m apping
of the contours was done on screen. Results w ere
validated in the field.
In the N etherlands, aerial inspection flights w ere
carried out in spring prior to the field survey to
assess w h e th e r the beds th at w ere m apped in the
previous year w ere still present, w h e th e r there
w ere n ew seed beds, and w h e th e r large portions of
beds had disappeared. Locations w here changes
w ere observed during flights w ere prioritized d u r­
ing the field surveys. The available tim e was u s u ­
ally no t sufficient to m ap all m ussel beds by m eans
of GPS. The percentage of bed locations th at w ere
surveyed in the field was betw een 40 an d 95% . For
beds n o t visited it was assum ed th at the contour is
the sam e as in the preceding year if the aerial
survey confirm ed the presence. W hen a bed is
visited in a following year the contour in the
m issing year is estim ated on the basis of the con­
tours before and after the missing year. The
Spatial Dynamics of Intertidal M ussel Beds
polygon vector files describing the contours of the
m ussel beds w ere im ported into a GIS. The online
supplem entary appendix A provides further details
of th e survey protocols and institutes.
In all th ree areas, according to th e TMAG p ro ­
tocol, beds qualify as m ussel beds if the percentage
cover by mussels is 5% or m ore. In th e period
1999-2010, m ussel beds m ay have contained Pa­
cific oysters (Crassostrea gigas) w hich w ere first ob­
served in th e w estern D utch W adden Sea (TB 39)
in late 1970s (Fey and others 2010; Troost 2010)
and in th e central W adden Sea (TB 26) in 1998 and
in th e n o rth ern W adden Sea (TB 4 and 5) in the
early 1990s (W ehrm an and others 2000). During
th e w in ter 2009/2010 ice-scouring destroyed
m ussel beds in Schleswig-Holstein (Büttger and
others 2011). To avoid bias caused by rem oval of
m ussel beds by ice-scouring, w e excluded from the
analysis grow th estim ates from TB 4-18 for the
year 2009.
The second data set is a com pilation of m ussel
bed contours for th e entire W adden Sea based on
aerial photographs recorded in 1968, 1973, 1974,
1975, and 1976 (Dijkema and others 1989). The
m ethodology used by Dijkema and others (1989)
differs from th e m ethodology used to obtain the
1999-2010 data set. In particular, m ussel beds w ere
dem arcated on th e basis of stereoscopy of aerial
photographs and the contours aro u n d m ussel beds
w ere draw n m ore loosely and w ider th a n for the
1999-2010 data set. A lthough com parison of
absolute areas betw een the data sets is not possible,
it is nevertheless possible to com pare spatial p a t­
terns in relative abundances.
Maps of th e intertidal areas and of land b o u n d ­
aries w ere provided by the Com m on W adden Sea
Secretariat (CWSS), W ilhelm shaven. The intertidal
area was assum ed to be constant over the period
1968-2010. A vector file representing the tidal
basins (Figure 1) was obtained from Kraft and
others (2011). All spatial data w ere organized in a
GIS. Lam bert A zim uthal Equal Area projection
(using th e ETRS89 datum ; EPSG code 3035) was
used to obtain tru e area representation over the full
study area.
To investigate th e effects of w inter tem perature
and storm iness on the dynam ics of m ussel bed
coverage w e m ade use of air tem perature and w ind
m easurem ents from different w eath er stations in
the W adden Sea area. The w eath er stations are all
located n ear the coastline (Figure 1). Germ an
tem p eratu re data w ere obtained from th e Germ an
M eteorological Service (w w w .dw d.de) for the sta­
tions List auf Sylt, C uxhaven, N orderney and Em ­
den. W e used G erm an w ind m easurem ents from
555
the stations List auf Sylt and N orderney only b e­
cause the storm iness at the stations C uxhaven and
Ernden is substantially low er th a n at the other
stations because they are located m ore land in ­
wards. D utch tem perature and w ind data w ere
obtained from the Royal N etherlands M eteorolog­
ical Institute (w w w .knrni.nl) for the stations Lauwersoog, H oorn Terschelling, Vlieland and De
Kooy. A m easure for w inter severity (T) was ob­
tained by calculating th e H ellm ann n u m b er w hich
is th e absolute value of the sum of the daily average
tem peratures below zero degree Celsius for the
period 1 Novem ber to 31 M arch. It is noted th at a
strong correlation betw een air- an d w ater-tem perature has been observed over a long period of tim e
in the w estern D utch W adden Sea (TB 39 in Fig­
ure 1) suggesting th at air tem perature is a solid
indicator for w ater tem perature (van A ken 2008).
We defined storm iness as th e n u m b er of days w ith
average w ind speed greater th a n 15 m /s (~ 7 Bit) in
the period 1 A ugust through 30 June. M ussel bed
grow th rates w ere related to th e Heilm an nu m b er
of the preceding w in ter (1 Novenrber-31 M arch)
and th e storm iness of the following period (1 August-30 April). For instance, grow th betw een 2005
and 2006 (and labelled R2oos) is related to the
H eilm an nu m b er over the period 1 N ovem ber 2004
and 31 M arch 2005 and the storm iness betw een 1
A ugust 2005 and 30 April 2006. For each tidal
basin w e used the w eath er data from the nearest
w eath er station (stations C uxhaven and Ernden are
excluded for w ind). The nearest w eath er station is
defined as the one w ith the shortest straight line
distance from the centroid of the tidal basin.
Statistical Models
Mussel Bed Area Per Tidal Basin and Spatial Clustering
We define m ussel bed area per tidal basin (A¡) as
the sum of th e areas of the m ussel bed polygons
intersecting w ith the tidal basins. M ussel bed cov­
erage per tidal basin i (C¡ in %) is th e percentage of
total intertidal flat area th at is covered by m ussel
beds. For the period 1999-2010 w e calculate yearly
sets of coverages and the m ean coverage over the
period; for the period 1968-1976 only one set of
m ussel bed coverage can be calculated.
Ord and Getis (1995) have developed tw o local
statistics, Gi an d G\, to evaluate the degree of spatial
clustering aro u n d a focal location, i. Statistic G¡
excludes location i front th e cluster w hereas G¡
includes it. The G and G* statistics express the sum
of th e values in th e vicinity of a particular location
as the proportion of the sum of the values for the
entire area. G and G* are positive w h e n the m ean of
556
E. O. Folm er an d others
a d u ste r is greater th a n the overall m ean and
negative w h e n it is less. Specifically, the statistics
identify those clusters of locations w ith values
higher or low er in m agnitude th a n m ight occur by
chance. G an d G* are similar to the z score in th at
th ey indicate how m any standard deviations a v a­
lue is above or below the m ean. For the m ean, the
standard deviation an d testing of the G and G*
statistics, see Getis (2009). We use the G* statistic
because w e are interested in the degree of cluster­
ing rath er th a n in the influence of observation i on
th e value of j (Getis 2009). We use the G* to
identify clusters of tidal basins w ith high and low
values of m ean m ussel bed coverage for the periods
1999-2010 an d 1968-1976. Following the notation
in Getis (2009) G] is defined as follows:
%
¿ W ij(d )C j _ i= l
w *c
-----
tidal basin i for the period 1999-2010. M ussel bed
grow th rates for the years 1999 to 2009 are calcu­
lated for the 25 tidal basins w ith m ean coverage
greater th a n 0.2% .
W e tested for density dependence an d the effects
of H ellm ann tem perature ( T ) of the preceding
w inter an d storm iness (S ) during the following
season on grow th rate (R) by m eans of a first-order
linear m odel (Royarna 1992). Because w e had no a
priori expectations of the functional form, w e also
considered n o nlinear relationships betw een the
dependent variable R and the explanatory variables
coverage (C), T and S by also including their
squared term s. We also considered interactions
betw een T and C an d betw een S and C to investigate
w h e th e r the effects of th e H ellm ann tem perature
and storm iness depend on m ussel bed coverage.
The first-order linear m odel can be w ritten as:
Ri,t = ßo + ß i Q,t + ßiTi,tp + ß^S i'tf + ß4Cft
+ ßs Tçtp + ßöSjtf + ß i Ci,tT¡,tp + ß &Q,tSi,tf
n
w * = X! WiiW and s*¡= X! wl for a11j
j=1
j= 1
w h ere w ¡j(d ) is the spatial w eight describing the
topological relationships betw een tidal basins i and
j as a function of the distance (d ) betw een their
centroids. It equals 1 if the distance is less th a n
30 1cm and 0 otherw ise (note th at w u= 1). We take
th e cut-off distance of 30 1cm to avoid isolation of
large tidal basins. A large cut-off distance w ould
lead to large num bers of neighbors for the smaller
tidal basins. The m axim um distance betw een cen­
troids of neighboring tidal basins is 28 lent (TB 37
and TB 36) an d th e shortest about 6 lent (TB 16 and
TB 15). C and s are th e m ean an d standard devia­
tion of th e coverages of m ussel beds for the 36 tidal
basins. A cluster of large positive values of G¡
indicates a hotspot of tidal basins w hereas a cluster
of large negative values indicates a cold spot.
Growth, Density Dependence, W inter Temperature and
Storminess
We calculated relative yearly m ussel bed grow th
rate per tidal basin i (R,-1) as the difference in the
percentage coverages of m ussel beds betw een suc­
cessive years divided by th e m ean coverage of tidal
basin i in th e period 1999-2010,
w h ere Q t is th e m ussel bed coverage in tidal basin i
in year t and Q is th e m ean m ussel bed coverage of
TLtp denotes the H ellm ann tem perature of the
w inter preceding tinte t in tidal basin i (e.g., grow th
in year 2005 depends on the tem perature of w inter
2004/2005). S l y denotes the storm iness during the
season following tinte t in tidal basin i (for example,
grow th in year 2005 depends on th e storm iness of
the season 2005/2006). W e used linear-m ixed ef­
fects m odels (LMM) to account for possible differ­
ences betw een tidal basins an d years. Particularly,
the effects of C, and S w ere allow ed to differ by tidal
basin an d year by including th em as random slopes.
We did no t m odel T w ith a random effect to keep
the nu m b er of variables w ith random effects low to
avoid unw ieldy models. The observations w ere
w eighted by Q to account for the fact th at the
relative changes in coverage are m ore precisely
determ ined in the tidal basins w ith large Q. Fol­
lowing, Bolleer and others (2009) w e used re ­
stricted m axim um likelihood (REML) estim ation.
The original distribution of R was leptokurtotic
w ith positive and negative tails w hich resulted in
heteroscedastic m odel residuals. W e therefore
norm alized the distribution of R by using th e pow er
transform ation (lRli sgn(R) — 1 ) /A ; A > 0 (Bickel
and Doksurn 1981). The value of X was selected on
the basis of the distribution of th e residuals. We
checked for norm ality and hom ogeneity by visual
inspections of plots of residuals against fitted val­
ues. W e set X = 0.5 w hich yielded satisfactory re ­
sults.
W e used th e following tw o-step m odel selection
procedure. Step 1 : We started by estim ating th e full
m odel w hich included all fixed effects and the
Spatial Dynamics of Intertidal M ussel Beds
random intercepts and slopes for C and S for both
th e levels "tidal basin" an d "y ear". W e rem oved
th e random slopes (from bo th levels) in order of
increasing standard deviation starting w ith the
random slope w ith the lowest standard deviation.
The successive m odels w ere com pared on the basis
of likelihood ratios. If the p-value for the likelihood
ratio test (LRT) was greater th a n 0.05, preference
was given to th e sim pler m odel. The random
intercepts for the levels "tidal basin" an d "y ear"
w ere retained to m eet th e assum ption of hornoscedasticity. Step 2: Given the obtained random
effects structure, w e estim ated all plausible nested
m odels by setting coefficients of the fixed effects at
zero (or rem oving their variables from the m odel).
Models including quadratic term s and interactions
w ere only considered if the low er order effects
w ere also in th e m odel. The estim ated m odels w ere
ranked in order of increasing AIC levels (B urnham
and A nderson 1998).
Synchronization
Synchronization is defined as th e correlation b e ­
tw een th e yearly m ussel bed grow th rates of the
tidal basins. We consider the correlation in yearly
grow th rate rath er th a n the correlation in cover­
ages because coverages m ay be affected by tem po­
ral trends (Turchin 2003). For each tidal basin pair,
synchronization is calculated using Pearson's cor­
relation coefficient (r). To avoid possible influence
of tidal basins w ith low m ussel bed coverages, the
correlation coefficients are calculated betw een tidal
basins w ith average coverage greater th a n 0.2% .
This resulted in a sym m etric 25 x 25 correlation
m atrix. The correlation m atrix for th e entire data
set—th at is, th e data set including the tidal basins
for w hich th e average coverage is less th a n or equal
to 0.2% —is presented in th e online appendix B.
We visualized the correlation m atrix by plotting
ellipses shaped as contours of a bivariate norm al
1999 -2 0 1 0
557
distribution w ith u n it variance and correlation r.
The points of th e contour are given by
(x,y) = (cos(0 + £ ), cos(0 —£)) an d d = arccos (r)
w here d e [0, 27i] (M urdoch an d Chow 1996).
W e used R 2.15.2 (R D evelopm ent Core Team
2009) for data handling, estim ation, and plotting.
For spatial data handling and statistics w e used the
R packages sp (Pebesma an d Bivand 2005; Bivand
and others 2008), rgeos (Bivand and R undel 2011),
rgdal (Keitt and others 2011). For m ixed m odeling
w e used th e R package lm e4 (Bates an d others
2012). For plotting w e used ellipse (M urdoch and
Chow 1996, 2012) and ggplot2 (W ickham 2009).
R esults
Mean M ussel Bed Coverage and Spatial
Stability
The m ean m ussel bed coverages strongly varied
betw een the tidal basins (Figure 1). The highest
m ean coverages w ere found in the tidal basins in
the eastern part of th e D utch W adden Sea (TB 3 1 36) an d in the w estern part of Lower Saxony (TB
22-TB 29). In some of the areas m ean coverage
ranged betw een 4 and 6% of the intertidal area.
The coverages w ere low in the n o rth ern tidal basins
in Schleswig-Holstein (TB 4-10) (betw een 0 and
1.5%) w hereas m ussel beds w ere virtually absent
in TB 11-18. The coverages w ere also relatively low
(< 0 .5 % ) in the w estern D utch W adden Sea (TB
37-39).
The spatial variation in m ussel bed coverages
presented in Figure 1 is m ore clearly visible in the
left panel of Figure 2 w hich shows the distribution
of m ussel bed h o t and cold spots (that is, th e G*
statistic) for the sam e period. The right panel shows
G* for th e 1968-1976 period. In bo th periods the
hotspots are situated in the eastern D utch W adden
Sea and in w estern
Lower Saxony w hile th e cold
spots are situated in n o rth ern Schleswig-Holstein
1968 - 1976
5
^
3
2
^
0
-1
-2
-3
F igure 2. Getis-Ord hotan d cold spot m aps for
average intertidal m ussel
bed coverages for th e
periods 1999-2010 an d
1968-1976 (Color figure
online).
558
E. O. Folm er an d others
and in th e w estern D utch W adden Sea. The highest
G* value occurred in w estern Lower Saxony in the
period 1968-1976 and in the eastern D utch W ad­
den Sea in th e period 1999-2010.
Temporal Trends and Synchronization
Figure 3 shows for each tidal basin the tren d in
m ussel bed coverage over th e period 1999-2010.
The yearly fluctuations are high in the tidal basins
w h ere th e average coverages are high and m uch
low er in th e tidal basins w ith low average cover­
ages. For exam ple, the coverages fluctuate irregu­
larly betw een 0 and 8% in the eastern Dutch
W adden Sea (TB 31-36) w hereas th e yearly ch an ­
ges in coverage in n o rth ern Schleswig-Holstein (TB
4, 6, 7, 9, and 10) are m ore regular and range b e ­
tw een 0 and 2.5% . Figure 3 also shows th a t tidal
basins in each o ther's vicinity show similar fluctu­
ation patterns. For instance, TB 22-26 in Lower
Saxony show a peak in th e year 2000 after w hich
th e coverages decrease u n til 2006 after w hich there
is a slight an d regular increase. In the eastern Dutch
W adden Sea (TB 31, 32, 34, 35, 36), a peak is ob­
served in 2002, after w hich th e coverages decline,
until a second peak is observed in m ost of th e tidal
basins in 2006. The grow th in the eastern D utch
W adden Sea in 2002 and 2006 is no t observed in
the G erm an (TB 4-30) or w estern D utch W adden
Sea (TB 37-TB 39).
Figure 4 shows th e correlation in m ussel bed
grow th betw een all tidal basins w ith average cov­
erages above 0.2% . The figure shows three blocks
of tidal basins w ith synchronic dynam ics. The m ost
northerly synchronic block (TB 4-10) is located in
n o rth ern Schleswig-Holstein. The second syn­
chronic block (TB 19-30) is bordered by the river
Elbe (TB 18) in th e east and by th e Erns estuary (TB
30) in the west. The dynam ics of th e relatively
small TB 20 are no t synchronized w ith the other
tidal basins in the block. The third block of syn­
chronic tidal basins (TB 30-39) is located in the
D utch W adden Sea. W ithin the three blocks the
correlation betw een tidal basins is highest w ithin
the centers of the blocks and decreases w ith dis­
tance betw een th e tidal basins. A lthough shortdistance synchronization dom inates, long-distance
synchronization is also observed betw een the
blocks consisting of TB 36, 37, and 39; and TB 19,
20, and 22, respectively.
Figure 3. Intertidal mussel bed coverage (%) of the tidal flats for 36 tidal basins for the period 1999-2010. The numbers in
the strips of the panels represent the tidal basin number (see Figure 1).
Spatial Dynamics of Intertidal M ussel Beds
O ^ O
^ - C O I ^ O ) - ! —
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559
) O W ( O t i r ) C D N C O O ) O ^ W ( O t l i ) 0 N O )
-I— C \ I C \ I C \ I C \ I C \ I C \ I C \ I C \ I C \ I C O O O C O C O C O C O C O C O C O
C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û C Û
I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I— I—
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tb6 ✓ /o/ooooooooo/oos%o%o%oo
tb7 o o OOOOOOOOOOOOOOOOOOOOOO
TB9
TB10
TB14
TB19
TB20
TB22
TB23
TB24
TB25
TB26
TB27
TB28
TB29
TB30
TB31
TB32
TB33
TB34
TB35
TB36
TB37
TB39
ooo ooooooxoooooooooooooo
0000 00000000000000 x00000
0)0000 o o o o o o o o o o o o o o o o o o o
o o o o o o
oooooooooooooooooo
o o o o o o o
00000000000000000
oooooooo //0 /00/000000000
O O O O O O O O /
0*0000000000000
000X0000/0 //OO0OOOOOOOOO
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00000000/0// 00/000000000
ooooooooooooo ooooooooooo
00O O 0O O O O O O O O O o o o o o o o o o o
00000000/000/00 OOOOOOOO0
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OOOOOOOO
/o o o o o o
o s o o o o o o o o o o o o o o o
o o o o o o o o o o o o o o o o o /
0/0000
O O 0 OO
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 / 0 0OOO
0 0 0 0 X 0 0 0 0 0 0 0 0 0 0 0 0 0 0
O O O O O O O O OOOOO OOOO OOO0 o o o
O O O O OO OO OOOOO OOOO OO0OO o o
O O O O O O O O O O O O O O O O O O O O O O O
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F igure 4. Ellipse m atrix of th e correlations b etw een yearly intertidal m ussel bed grow th rates of th e tidal basins for w hich
th e average coverage w as greater th a n 0.2% . The ellipses are th e contours of a bivariate n o rm al distribution w ith u n it
variances an d Pearson's correlation coefficient r (M urdoch an d Chow 1996). In th e case of positive correlation, th e color of
th e ellipse is blue. In th e case of negative correlation, th e color is red. The intensity of th e color relates to th e (absolute) size
of th e correlation coefficient. Correlations of th e elem ents along th e diagonal are always 1 an d n o t inform ative an d th u s
n o t presented. The ellipse m atrix for all tidal basins is presen ted in th e supplem entary appendix B (Color figure online).
M ussel Bed Growth Models
In th e full m odel of step 1—th at is, the m odel
including all fixed effects and random intercepts
and ran d o m slopes for coverage and storm iness at
th e levels tidal basin and year—th e relative stan ­
dard deviation of the random slope of coverage in
level tidal basin was small. Accordingly, the likeli­
hood ratio test show ed th at deleting this random
effect from th e full m odel did not cause a significant
reduction in likelihood and thus preference was
given to th e simpler m odel (Table 1, m odel 4). This
elim ination procedure was continued until only
th e intercept at the levels of tidal basin and
storm iness at the year level rem ained as random
effects. On th e basis of the thus obtained random
effects m odel (that is, random intercept for tidal
basin level and storm iness at the year level) all
nested fixed effects m odels w ere estim ated and
ordered by AIC (Table 2). The best m odel only in ­
cludes a negative term for C suggesting th at
negative density-dependent processes are acting
(—0.160, SE 0.024). The second best m odel in term s
of AIC—w hich is considerably w orse th a n the
best m odel (AAIC = 4.486)—indicated in addition
to a negative effect of C a slight positive b u t highly
insignificant effect of storm iness (S). The third best
m odel (AAIC = 5.512) also indicated a negative
effect of C an d a positive effect of C2 suggesting th at
the negative effect of coverage leveled of at high
values of C. Models 4 and 5 fit th e data substantially
w orse th a n m odels 1-3 (AAIC > 7.859). M odel 4
shows a negative effect of S and a positive effect of
S2 in addition to the negative C effect. M odel 5
shows a positive b u t highly insignificant effect of S
b u t a negative an d significant interaction betw een
C and S. W inter severity (T) does n o t show up as a
predictor in the models 1-5. The variance of the
random effects at the year level indicated consid­
erable heterogeneity across years w hile the vari­
ance across tidal basin is very low.
560
T able 1.
E. O. Folm er an d others
S e le c tio n of R a n d o m E ffects of th e L in e a r M ix e d M o d e ls of M u s se l B e d G ro w th
M odel
TB level
SD
Year level
SD
1
Intercept
Residual
Intercept
0.232
Intercept
0.100
Intercept
0.341
0.958
0.706
0.279
0.922
0.707
0.106
0.296
0.882
0.587
0.129
0.254
0.856
1.514
0.258
2.521
0.844
2
S
3
Residual
Intercept
0.153
Intercept
C
S
4
Residual
Intercept
C
5
Residual
Intercept
C
S
0.326
0.128
Intercept
0.227
0.103
0.045
Intercept
C
S
C
S
Residual
X2
Df
P value
21.421
2
< 0 .0 0 0
2.141
3
0.544
3.375
2
0.185
0.000
3
1.000
C = intertidal mussel bed coverage (% ); S - storminess; TB = tidal basin. In this stage o f the model selection procedure the fixed effects (C, C2, T, I 2, S, S2, C x S, C x T)
remain unchanged. The column SD gives the standard deviations fo r the random effects o f the intercept, C and S for the levels TB and Year. The columns y 2, D f and the P value
present the relevant outcomes o f the y f tests. The num ber o f observations is 269 after removing the observations that were possibly influenced by ice-scouring; the num ber o f
tidal basins is 25 (viz. the ones that fit the criterion o f mean coverage greater than 0.2%) and the number o f years is 11.
D is c u s s io n
and
C o n c l u s io n s
We analyzed th e spatial variability of the coverage
of m ussel beds in the international W adden Sea on
th e basis of data collected in the periods 1968-1976
and 1999-2010. W ithin each period, notable re ­
gional differences in m ussel bed coverages w ere
observed. B etw een the periods, how ever, these
distribution patterns w ere rem arkably stable in th at
th e regions th at w ere densely populated in the
period 1968-1976 w ere also densely populated in
th e period 1999-2010. The highest coverages occur
in th e relatively small an d sheltered tidal basins in
th e eastern D utch an d in th e w estern Lower Saxon
W adden Sea w hereas m ussel beds w ere virtually
absent in tidal basins th a t are n o t sheltered by
barrier islands (TB 11-18). The strikingly stable
m ussel bed distributions m ay be explained by the
regionally variable levels of exposure to hydrody­
nam ic forces w hich are relatively constant on the
scales of decades.
A lthough analysis of long-term regional differ­
ences in m ussel bed coverage provides insight into
the relatively invariable determ inants of habitat
suitability, analysis of year-to-year variability
including spatial synchrony, sheds light on the
determ inants of short-term dynam ics. W e expected
th at yearly increases in coverage w ould be ex ­
plained by episodic recruitm ent events following
cold w inters and th at decreases w ould occur during
storm y w in ter seasons. In particular, previous
studies perform ed at various locations have show n
th at bivalve recruitm ent events are often preceded
by cold w inters (Möller 1986; Young and others
1996; B eukem a and others 2001; Strasser and
others 2003) w hich has led to th e com m only held
view th at w in ter severity is th e m ain driver of b i­
valve recruitm ent events and population synchro­
nization in N orth W est Europe. Because w inter
tem peratures are strongly correlated in the entire
study region (Figure 5) w e hypothesized th at
grow th peaks w ould show up at the same tim e
th ro u g h o u t th e entire study area. If regional
grow th determ inants or larval dispersal w ould
dom inate, th e n population synchronization w ould
show up at sm aller spatial scales. Our analyses did
n o t show th at cold w inters system atically induced
increases in m ussel bed coverages. Nor did w e find
strong evidence for negative effects of storminess.
We did find, how ever, th at synchronic grow th was
constricted to confined blocks of tidal basins b o r­
dered by hydrodynam ic barriers such as rivers and
large extents of unsuitable habitats. These results
point tow ard th e im portance of considering larval
transport an d early stage survival to fu rth er d e­
velop understanding of the spatial dynam ics of
m ussel beds in th e W adden Sea. The im portance of
larval transport has also been dem onstrated for
coastal m ussel populations in southw est England
(Gilg and Hilbish 2003) and th e southeast South
Africa (M cQuaid and Phillips 2000) w here coastal
Spatial Dynamics of Intertidal M ussel Beds
561
T able 2. Regression Table w ith th e Full and th e Five Best Mixed-Effects Models for M ussel Bed Grow th Rate
Selected on th e Basis of AIC Values
F u ll M odel
Intercept
C
C2
S
S2
T
T2
C x S
C x T
R andom effects
Intercept for TB
Intercept for year
S for year
residual
AAIC
AIC
BIC
Deviance
Log-likelihood
-1 .6 1 8
(-0 .3 0 3 )
-0 .2 7 7
(-0 .0 7 8 )
0.028
(0.009)
-0 .1 1 4
(0.169)
0.046
(0.029)
-0 .0 0 1
(0.008)
-0 .0 0 0
(0.000)
-0 .0 4 0
(0.016)
-0 .0 0 0
(0.001)
M o d el 1
-1 .8 9 4
(0.098)
-0 .1 6 0
(0.024)
M o d el 2
-1 .9 5 5
(0.229)
- 0 .1 5 8
(0.024)
M o d el 3
-1 .7 3 0
(0.124)
-0 .3 2 2
(0.072)
0.024
(0.009)
0.024
(0.087)
M o d el 4
M o d el 5
-1 .7 1 8
(0.223)
-0 .1 3 6
(0.024)
-2 .0 7 9
(0.234)
-0 .0 9 9
(0.034)
-0 .2 9 0
(0.142)
0.069
(0.026)
0.111
(0.094)
-0 .0 4 1
(0.015)
0.010
0.498
0.078
0.851
0.019
0.427
0.061
0.868
990.216
1040.542
962.216
-4 8 1 .1 0 8
930.489
955.651
916.489
-4 5 8 .2 4 4
0.019
0.471
0.068
0.868
4.486
934.975
963.733
918.975
-4 5 9 .4 8 8
0.012
0.416
0.061
0.864
5.512
936.001
964.758
920.001
-4 6 0 .0 0 0
0.015
0.360
0.060
0.866
7.859
938.348
970.700
920.348
-4 6 0 .1 7 4
0.023
0.470
0.070
0.856
8.188
938.677
971.030
920.677
-4 6 0 .3 3 9
C = mussel bed coverage (% ); S - storminess; TB = tidal basin. Standard errors in parentheses underneath the regression coefficients. The num ber o f observations (269) is the
same as in Table 1.
circulation is an im portant determ inant of larval
dispersal. A lthough our hypothesis and results are
consistent w ith findings from other systems, w e do
n o t rule o u t alternative explanations. For instance,
regional differences in the change of eutrophication
(van B eusekom and others 2012) an d carrying
capacity or predation rates could lead to regional
differences in vital rates w hich could cause the
observed synchronization patterns. A nother possi­
ble cause of diverging dynam ics in the separate
blocks m ay be related to local hydrodynam ic dis­
turbances w hich w ere no t detected by regressing
adult m ussel bed dynam ics on storm iness. To dis­
tinguish betw een th e above-m entioned hypotheses
it is im portant to obtain detailed long-term spatial
data on com petition and food lim itation, rep ro ­
ductive capacity and output, early stage predation,
recruitm ent,
hydrodynam ic disturbance and
transport. In this context it is also im portant to
have better u n derstanding of the positive and
negative interactions betw een Pacific oysters and
mussels. Particularly, mussels and oysters benefit
from each other in that they provide suitable substrate
but they suffer from each other due to competition for
food resources. The long-term consequences of the
establishment of Pacific oyster populations for mussel
populations will depend on the responses of both
populations to environm ental conditions and on the
interactions betw een the populations.
The fact th at w e did n o t find a tem perature effect
on grow th does n o t im ply th at tem perature m ay
n o t have an effect on the long-term dynam ics and
sustainm ent of intertidal m ussel beds at all. There
are several reasons w h y w e m ight have missed to
detect a w in ter tem perature effect. Firstly, our tim e
series covered the period 1999-2010 w hich was
relatively w arm . B eukem a an d others (2001), w ho
did find a tem perature effect, used data for the
period 1969-1999 w hich contained several cold
w inters. In particular, the severe w inters of 1978/
1979 an d 1995/1996 w ere followed by extraordi­
n ary recruitm ent events for different species of
562
E. O. Folm er an d others
150 -
F igure 5. W inter severity
an d stornriness for the
different w ea th er stations
along th e W adden Sea
coast. W inter severity or
th e H ellm ann
W e a th e r s ta tio n
List a u f Sylt
-*■ C u x h a v e n
-*■ N o rd e rn e y
E m den
L a u w e rs o o g
-*■ H o o rn T e rsc h e llin g
-»■ V lieland
-*■ D e K ooy
-+■
g 100
CD
CD
i—
B
temperature was
c
5
50 -
o98/99
10.0
99/00
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
08/09
W e a th e r s ta tio n
-
-*■ List a u f Sylt
-*■ N o rd e rn e y
L a u w e rs o o g
-*■ H o o rn T e rsc h e llin g
-*■ V lielan d
-*■ D e K ooy
7.5 CD
CD
CD
■|
09/10
calculated as th e absolute
value of th e sum of th e
daily average
tem peratures th a t w ere
below zero degrees
Celsius for th e period 1
N ovem ber-31 M arch.
Storm iness is defined as
th e n u m b e r of days w ith
average w in d speed
greater th a n 15 nr/s
(~ 7 Bft) in th e period 1
A ugust-30 Ju n e (Color
figure online).
5.0 -
1—
o
CO
2.5 -
0.0
-
98/99
99/00
00/01
01/02
02/03
03/04
04/05
05/06
bivalves including M ytilus edulis. The study of
M öller (1986) also included th e year 1979 after
w hich an eightfold increase in th e num erical d en ­
sity of M ya arenaria was observed. The study of
Strasser and others (2003) focused on recruitm ent
of several species of bivalves in the w hole W adden
Sea following th e cold w in ter of 1995/1996. They
found higher th a n average densities of recruits of
Cerastoderma edule in 1996 in the entire W adden
Sea. For th e species Macoma balthica an d M ya are­
naria higher th a n average recruitm ent was only
observed in th e southern W adden Sea. It is th e re ­
fore possible th at w e w ould have found statistically
significant tem p erature effects if our tim e series had
included m ore and colder w inters. It is im portant to
note, how ever, th at cold w inters are no t a p re ­
requisite for high recruitm ent events as relatively
m ild w inters m ay also be followed by high
recru itm en t events. For instance, after the rela­
tively m ild w inters of 1974/1975 and 1990/1991,
B eukem a and others (2001) found high densities of
recruits for different species of bivalves. A nd in our
data set th e m ild w inters of 2000/2001 and 2004/
2005 w ere followed by substantial increases of
06/07
07/08
08/09
09/10
adult m ussel beds in th e D utch W adden Sea (ob­
served as peaks in 2002 an d 2006). Because cold
w inters are expected to occur less frequently in the
future due to clim ate change, the factors u n d erly ­
ing recruitm ent after relatively w arm w inters will
becom e increasingly im portant determ inants of
bivalve m etapopulations. A nother potential cause
of th e absence of a w in ter tem perature effect is th at
loss processes operate betw een th e m om ents of
recruitm ent an d th e surveys of adult m ussel beds
w hich m ay distort the correlation betw een w inter
tem perature and m ussel bed grow th to som e ex ­
tent. This implies th at w inter tem perature effects
m ight be m ore easily detected for endobenthic
species such as Macoma balthica th a n for epibenthic
species th at are m ore vulnerable to hydrodynam ic
disturbance. Note, how ever, th at w e did observe
bivalve-characteristic episodic grow th peaks by
considering yearly changes in the coverage of adult
m ussel beds (Figure 3).
The absence of evidence of a stornriness effect in
the grow th m odel does no t im ply th at stornriness
does n o t influence m ussel bed dynam ics. The ab ­
sence of a stornriness effect m ay be explained by
Spatial Dynamics of Intertidal M ussel Beds
th e fact th at w e considered dynam ics of adult
m ussel beds w hile especially settlem ent an d new ly
settled m ussel beds are sensitive to hydrodynam ic
disturbance and stornriness because individual
m ussels have n o t yet attached them selves firm ly to
th e sedim ent an d each other. As a result estab­
lishm ent and developm ent of adult m ussel beds
tends to occur at relatively sheltered locations
w h ere th e im pact of storms on adult beds is rela­
tively low. For exam ple, Nehls and Thiel
(1993)—w ho studied survival of m ussel beds
including new ly recruited m ussel beds—found th at
only 49 o u t of 94 beds survived their first w inter
and th at all of the surviving beds w ere located in
sheltered areas. These areas w ere also found to be
populated w ith m ussel beds in several decades b e ­
fore and after their investigation. Our paper con­
cerns th e dynam ics of adult m ussel beds w hich
implies th at w e are analyzing th e dynam ics of beds
th at already survived the initial young stages and
th u s occur in relatively sheltered locations only.
The lim itation of our em pirical approach to d e­
tect stornriness effects could be cleared up by
integrating observations of young and adult in te r­
tidal m ussel beds w ith hydrodynam ic models
(Donker and others 2012). A lthough such in te ­
grated m odels provide insight into th e m echanic
interactions b etw een hydrodynam ic forces and
intertidal m ussel beds, the approach will be difficult
to apply to large tem poral and spatial scales for the
following tw o m ain reasons. Firstly, the hydrody­
nam ic conditions of all the relevant loca­
tions—w hich include tides, w ind and fetch—need
to be k n o w n w hich requires very detailed data sets,
extensive m onitoring and com plex and com puter
intensive m odeling. Secondly, because w aves and
currents are atten u ated w h e n they travel over the
rough m ussel bed surfaces, they cannot be con­
sidered in d ep endent variables. Yet, it w ould be
useful to have m ore insight into the long-term
impacts of waves and currents on the tidal flats in
order to identify suitable m ussel bed habitat in
term s of hydrodynam ics independently of the
observations presented in this paper.
In this paper, w e have suggested th at synchronic
spatial population dynam ics w ithin blocks of
neighboring tidal basins m ay result from distancedependent dispersal of larvae. In particular, larvae
th at are produced in one tidal basin m ay be tran s­
ported to hydrodynam ically connected tidal basins.
This suggestion is supported by the fact th at the
borders of th e synchronic blocks are located at tidal
basins w ith estuaries an d rivers (in w hich w ater
currents form a barrier tow ard the next tidal b a ­
sins) an d at regions th at lack suitable habitat. As
563
m entioned above, th e tu rn o v er tim e for th e Erns
estuary (Figure 1, TB 30) is substantially longer
th a n the tu rn o v er times for the other D utch tidal
basins (Ridderinkhof an d others 1990) so th a t the
chance for a larva to survive the tim e it takes to
pass the estuary and arrive at suitable habitat is
relatively low. The chance to survive the passing of
the Elbe estuary (TB 18) an d arrive at suitable
habitat w ould conceivably be even lower. Our re ­
sults, th at is, th at th e population dynam ics w ithin
the blocks separated by th e Erns an d Elbe estuaries
are distinct, are in line w ith this observation.
H ydrodynam ic transport m odeling, on the basis of
tides and w ind, could elucidate possible fates of
m ussel larvae and provide insight into the con­
nectivity of suitable habitat patches w ith in tidal
basins. This type of m odeling w ould help to provide
insight into the structure of the nretapopulation
netw ork and w ould be able to show w hich sub­
populations produce larvae w ith high survival and
settling probability. In addition to w ater m ove­
m ent, the characteristics of the w ater affecting
larval grow th and developm ent (such as food
availability, salinity, an d tem perature) should be
considered (M etaxas and Saunders 2009). Particu­
larly, a low er grow th- an d developm ent rate during
the larval phase increases th e tim e a larva needs to
com plete developm ent w hich increases the risk of
being predated by ctenophores and benthic feeders
or of being transported offshore (Young and others
1998; Philippart and others 2003; M etaxas and
Saunders 2009). H ydrodynam ic connectivity m o d ­
eling and w ater quality m easurem ents in com bi­
n ation w ith larval requirem ents could provide a
fram ew ork for the developm ent of a dispersal
m odel describing the corridors betw een suitable
habitats from th e perspective of larvae.
In th e W adden Sea there is high quality, com ­
parable long-term distribution data available for
intertidal m ussel beds b u t n o t for subtidal m ussel
beds. Because our quantitative analyses required
hom ogeneous data w e have focused on intertidal
m ussel beds only. Subtidal m ussel populations
may, how ever, influence the dynam ics of intertidal
populations by producing larvae th at end up in the
intertidal an d vice versa. Hence, a com plete m odel
of spatial population dynam ics w ould include su r­
vival, production, dispersal, and settlem ent of b oth
intertidal and subtidal populations.
The spatial configuration of suitable habitat and
population grow th dynam ics presented in this p a ­
per is relevant from a n atu ra l resource m an ag e­
m en t perspective. If it is com bined w ith the
outcom es of fu rth er research suggested above, it
will be possible to pinpoint those locations and
564
E. O. Folm er an d others
sub-populations th at are of pivotal im portance for
th e resilience of the nretapopulation of m ussel beds
and hence require special protection.
ACKNOWLEDGMENTS
This study was financially supported by the W ad­
denfonds, province of Noord Holland and the Pro­
gram tow ards a rich W adden Sea. W e th a n k the
following organizations for supporting the in te r­
tidal m ussel bed surveys and providing data: 1. The
Schleswig-Holstein Agency for Coastal Defence,
National Park an d M arine C onservation—National
Park A uthority, 2. N ational Park A dm inistration
W adden Sea Lower Saxony an d The Lower Saxon ian Agency for W ater M anagem ent, Coastal Pro­
tection and N ature Conservation, 3. IMARES. The
A lfred-W egner-Institute (Sylt) kindly provided
aerial photographs. W e th a n k D ietm ar Kraft for
constructing th e vector file w ith the tidal basins
and Carola van Zweeden for GIS database m a n ­
agem ent of IMARES data. We are very grateful to
Folieert de Jong for stim ulating an international
and com parative approach. W e th an k our col­
leagues Jaap van der Meer, Ulrich Callies, Theunis
Piersma, Sofia Saraiva and David Thieltges for dis­
cussion and Jaap van der M eer, Jan B eukem a and
Peter H erm an for providing challenging and v alu­
able com m ents on th e m anuscript. Finally, we
th an k th e anonym ous review er and th e nonanonym ous review er Dr. M artin Thiel for their
critical and useful com m ents.
OPEN ACCESS
This article is distributed u n d er th e term s of the
Creative Com m ons A ttribution License w hich
perm its an y use, distribution, and reproduction in
any m edium , provided th e original author(s) and
th e source are credited.
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