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ORIGINAL PAPER

Combining gut fluorescence technique and spatialanalysis to determine Littorina littorea grazing dynamicsin nutrient-enriched and nutrient-unenriched littoral mesocosms

Eliecer Rodrigo Dı́az • Patrik Kraufvelin •

Johan Erlandsson

Received: 17 June 2011 / Accepted: 15 December 2011 / Published online: 18 January 2012

� Springer-Verlag 2012

Abstract Spatiotemporal distribution patterns in relation

to feeding behavior of herbivorous gastropods have been

studied extensively, but still knowledge about small-scale

patterns is limited in relation to eutrophication. This

experimental study aimed to describe the small-scale dis-

tribution of Littorina littorea in nutrient-enriched and

nutrient-unenriched mesocosms in a merely atidal region

and relate the distribution to food abundance and possible

competing organisms, while checking simultaneously for

feeding activities. The latter part was accomplished

through the ‘‘gut fluorescence technique’’ GFT (which, to

our knowledge, has not previously been used for benthic

grazers) to estimate per capita grazing rates and the former

part through monitoring of spatial heterogeneity of L. lit-

torea and co-variation with sessile organisms (using

semivariograms and cross-semivariograms, respectively).

After 5 months of nutrient addition, the abundance and

biomass of L. littorea had increased in enriched systems,

which also had significantly higher total biomass of green

algae. Gut pigment content was higher in L. littorea from

enriched mesocosms, and gut depletion rate was higher in

L. littorea from unenriched mesocosms. Spatial analysis

showed that L. littorea exhibited generally random patterns

(suggesting feeding activities) but sometimes (often in the

morning) spatial patchiness (clumped distribution) in both

enriched and unenriched conditions. There was mainly

positive co-variation between L. littorea and biofilm, while

different nutrient conditions exhibited contrasting co-vari-

ation between L. littorea and barnacles (positive co-varia-

tion in enriched and negative co-variation in unenriched

mesocosms). The study offered insights into how feeding

behavior and spatial distribution of a species may interact

with community components differently under different

nutrient regimes. The applied methodology can be useful

for purposes of faster examination of grazing effects

among different regions and also to compare grazing

intensities and interactions between grazers and the benthic

communities in disturbed (including pollution and nutrient

enrichment) and non-disturbed systems, as well as in up-

welling versus non-upwelling areas.

Introduction

A principal challenge for experimental ecology is to

develop techniques that allow fast, but reliable, assess-

ments of ecosystem variables, such as primary productiv-

ity, diversity, and trophic interactions. In this study, we

present a combination of two techniques (the gut fluores-

cence technique, GFT, and spatial analysis) that can help to

disentangle trophic dynamics and species distribution pat-

terns in benthic systems. Nutrient enrichment and changes

in grazer populations often interact to shape diversity and

biomass of benthic macroalgal assemblages and primary

consumers (Lubchenco and Gaines 1981; Hillebrand 2003;

Communicated by F. Bulleri.

E. R. Dı́az � P. Kraufvelin (&) � J. ErlandssonARONIA Coastal Zone Research Team, Å

´bo Akademi

University and Novia University of Applied Sciences,

Raseborgsvägen 9, 10600 Ekenäs, Finland

e-mail: [email protected]; [email protected]

P. Kraufvelin

Environmental and Marine Biology, Åbo Akademi University,

Artillerigatan 6, 20520 Turku/Åbo, Finland

Present Address:J. Erlandsson

Vattenmyndigheten, Västerhavets Vattendistrikt,

Vattenvårdsenheten, Länstyrelsen i Västra Götalands län,

403 40 Göteborg, Sweden

123

Mar Biol (2012) 159:837–852

DOI 10.1007/s00227-011-1860-y

Gamfeldt et al. 2005; Eriksson et al. 2009) and thereby also

the ecosystem functioning (McQuaid 1996; Paine 2002;

Worm et al. 2002; Griffin et al. 2010; Kraufvelin et al.

2010). Although spatial and temporal distribution patterns

of herbivorous gastropods in relation to their feeding

behavior have been studied extensively on rocky shores

(Hawkins and Hartnoll 1983; Little 1989; Little et al. 1991;

Gray and Naylor 1995; Johnson et al. 1997; Coleman et al.

2006; Diaz et al. 2011), knowledge about small-scale pat-

terns and species interactions is still limited in relation to

eutrophication. Changes in benthic primary productivity,

community composition, and species abundance caused by

eutrophication, however, are generally well known (e.g.,

Cloern 2001; Burkepile and Hay 2006; Kraufvelin et al.

2006b, 2010; Russell and Connell 2007; Masterson et al.

2008). Macroalgal-dominated littoral communities possess

a high structural and functional resistance against excessive

nutrient availability as long as the communities are not

seriously affected by other chemical, physical, or biologi-

cal processes (Connell 1985; Thompson et al. 2002; Bokn

et al. 2003; Worm and Lotze 2006; Kraufvelin et al. 2006b,

2010). Part of this resistance to mass occurrences of

opportunistic macroalgae has been explained by grazing

macroinvertebrates such as the common periwinkle Litto-

rina littorea (L.) (Kraufvelin et al. 2002), other molluscs

(Russell and Connell 2007) as well as the amphipod

Gammarus locusta L. (Kraufvelin et al. 2006a) buffering

eutrophication effects by exerting strong top-down control.

The understanding of the feeding behavior and ecology

of gastropods, such as L. littorea, is instrumental for

understanding the community structure of the shores that

they inhabit (McQuaid 1996; Carlson et al. 2006). L. litto-

rea is found on rocky shores at the East and West Atlantic

coasts, preferentially at low shore levels (Norton et al. 1990;

Carlson et al. 2006; Perez et al. 2009). Although the feeding

preferences of L. littorea are mainly restricted to early

successional stages of perennial macroalgae, diatoms, and

ephemeral green algae (Norton et al. 1990; Wilhelmsen and

Reise 1994; Jaschinski and Sommer 2011), the species has

also been characterized as an omnivorous grazer (Chang

et al. 2011) that can even feed on barnacle larvae (Wahl and

Sönnichsen 1992; Buschbaum 2000). The feeding activity

of littorinids seems to be influenced by several factors such

as body mass, water temperature, and tides (Newell et al.

1971; Norton et al. 1990). This leads to the question of

whether L. littorea has an endogenous rhythm preferring

feeding during the night to avoid predators and desiccation.

Normally, L. littorea feeds when substrates are damp or

when it is submersed (Norton et al. 1990) and during that

time the gastropods exhibit less aggregation and their guts

contain more food. When littorinid snails are inactive (e.g.,

on dry substrates), they tend to group in crevices, among

mussels and barnacles or other architecturally complex

microhabitats forming clumps at different shore levels

(Raffaelli and Hughes 1978; Chapman and Underwood

1996; Kostylev et al. 1997; Chapman 2000; Diaz et al.

2011; Erlandsson et al. in prep.). In situations with nutrient

enrichment, primary productivity in terms of biofilms and/

or green algae will generally be enhanced, which may imply

increased food availability or more nutrient rich food. This

could, in turn, shorten the browsing distances and periods of

L. littorea, only having to move a few centimeters away

from the aggregations to reach feeding spots or being able to

spend far less time foraging.

Our examination of the relationships between feeding

activities and spatial aggregation of L. littorea in nutrient-

enriched and nutrient-unenriched mesocosms comprised the

study of its feeding (e.g., ingestion rates), the estimation of its

spatial heterogeneity, and analysis of its relationships with

other species components in the community. The feeding

activity of a herbivorous gastropod could either be investi-

gated by indirect or by direct methods. The indirect methods

are studying gastropod movements over time or grazer

exclusion by cages (Newell et al. 1971; Underwood 1980;

Underwood and Jernakoff 1984; Boaventura et al. 2002;

Hutchinson and Williams 2003; Coleman et al. 2006),

whereas the ‘‘gut fluorescence technique’’ (GFT) represents a

direct method. GFT is one of the most broadly used methods

in pelagic systems and it takes gut content, time of digestion,

and defecation processes directly into account. It has been

successfully used to estimate grazing activity of zooplankton

in a variety of aquatic habitats (Mackas and Bohrer 1976;

Bernard and Froneman 2005). The principle behind the

technique is that algal pigments ingested can be quantita-

tively extracted from the animal using organic solvents

(Båmstedt et al. 2000). The main benefit of the technique is

that it is possible within 24 h to obtain data about how much a

particular species consumes. GFT estimates grazing activity

through the quantification of the ‘‘daily ingestion rate,’’ which

contains three variables that can be experimentally obtained:

(1) integrated gut pigment, (2) gut depletion rate, and (3) gut

pigment destruction. In spite of the simplicity of GFT, it has,

to our knowledge, not been tested previously in benthic sys-

tems. Following estimation of the temporal feeding activities

of L. littorea snails by GFT, geostatistical tools were used to

assess their spatial aggregations for the very same time

periods. Within this process, semivariograms and fractal

dimension were first used to distinguish between spatial

patchiness and randomness (see Diaz and McQuiad 2011;

Diaz et al. 2011 for distribution of grazers) and then cross-

semivariograms were used to examine the co-variation

between L. littorea and different community components

(barnacles, biofilm, and macroalgae).

The central aims of the present study were to document

responses to nutrient enrichment and determine grazing

dynamics of L. littorea and its co-variation with the rocky

838 Mar Biol (2012) 159:837–852

123

shore community under controlled nutrient-enriched and

nutrient-unenriched conditions. The following hypotheses

were tested: (1) The abundance and biomass of L. littorea

and the biomass of green macroalgae will be stimulated after

5 months of nutrient enrichment. (2) The combination of

GFT (feeding) with semivariograms (spatial distribution)

will show that L. littorea feeds (and disperses) differently

during the day/night cycle and at different nutrient condi-

tions. (3) There will be spatial co-variation (negative or

positive) between L. littorea and other dominant organisms

(barnacles, biofilm, and macroalgae), and this co-variation

may be expressed differently at different nutrient levels. All

parts of the study sum up to provide novel information about

responses to nutrient enrichment, the trophic dynamics of

L. littorea on nutrient-enriched and nutrient-unenriched

temperate rocky shores as well as about interactions between

the gastropod and its surrounding community.

Materials and methods

Solbergstrand rocky littoral mesocosms

All measurements were made in eight rocky littoral meso-

cosms at Marine Research Station Solbergstrand by the

Oslofjord (59�370N, 10�390E) in SE Norway. Each meso-cosm had a length of 4.75 m, a breadth of 3.65 m, and a

maximum depth of 1.35 m (Fig. 1). Throughout this study,

the systems were kept non-tidal, since natural shores in the

region are basically atidal (tidal amplitude ca 0.35 m) and

we wanted specifically to control for all other factors to

ascertain that the observed effects were due to the nutrient

treatments. The water volume was 12 m3, and each flow-

through mesocosm received water from 1 m depth from the

Oslofjord at a rate of 5 m3 h-1 and with a short mean water

residence time of 2–3 h. A wave machine generated con-

stant waves (18 strokes per minute) with 11 cm amplitude

corresponding roughly to a wind force of up to 5 m/s

(Kraufvelin et al. 2010). The entire mesocosm facility was

covered with a transparent black net in order to reduce the

light and UV effects (by approximately 50%) down to the

bottoms of the mesocosms, where sugar kelp, Laminaria

saccharina (L.) J.V. Lamouroux was grown within a sep-

arate scientific project run simultaneously. With regard to

macroalgae and L. littorea, including gastropod behavior

(Kraufvelin et al. unpubl.), the mesocosm conditions

resembled very closely natural conditions on semi-sheltered

concrete walls and rock pools on shaded shores right outside

Solbergstrand, that is, in the middle parts of the Oslofjord.

At the time of these experiments, the history of the

rocky littoral communities of individual Solbergstrand

mesocosms dated back [12 years. Rocky shore assem-blages were introduced in 1996 by transplanting small

boulders with attached macroalgae and associated animals,

onto concrete steps in each mesocosm, and with time,

mesocosm communities have been corresponding well with

natural rocky shores in the region. (Bokn and Lein 1978;

Bokn et al. 2003; Kraufvelin et al. unpubl.). The mesocosm

experiments, which this study is part of, were run from

April to September 2008 and comprised nutrient addition

to four mesocosms, while the remaining four served as

background controls receiving only fjord water. Before the

start of the experiments, all mesocosms were evened out as

described in Kraufvelin (2007), that is, the amount and type

of macroalgae and, for example, the abundance of L. lit-

torea were registered in each mesocosm and from meso-

cosms where they were in excess, individuals were moved

into mesocosms, where the occurrences were lower. These

measures also ensured that there were no carry over

influences from previous experimentation.

Enriched mesocosms were treated with 32 lM inorganicnitrogen (N) and 2 lM inorganic phosphorus (P) above thebackground levels in the Oslofjord (for which monitoring

data was provided by Norwegian Institute for Water

Research) continuously in the period April–September

2008. These nutrient addition levels are similar to con-

centrations recorded in eutrophic areas locally (Kristiansen

and Paasche 1982) and globally (Cloern 2001), and cor-

responding nutrient addition levels have been utilized as

‘‘highs’’ during previous experiments in these mesocosms

(Bokn et al. 2002, 2003; Kraufvelin et al. 2006a, b, 2010).

Nutrients were added as a mixture, which consisted of

14.3 mol N as NH4NO3 and 0.9 mol P as H3PO4 and an

N/P mol ratio of 16/1. The actual nutrient concentrations of

the mesocosm water were not analyzed on a regular basis,

but it could be ascertained from day to day that the desired

nutrient concentrations were achieved thanks to constant

monitoring of the amounts of nutrients that were auto-

matically pumped up from separate trays for each enriched

mesocosm. The two nutrient treatment levels were inter-

spersed among the mesocosms, but in a systematic way

instead of randomly to avoid the risk of getting too many

parallel treatments at one end of the mesocosm facility.

Determination of abundance and biomass of L. littorea

and green algae

In September 2008, the abundance and biomass of L. lit-

torea on the northern (sunny) walls and the total amount of

green algae were estimated in each mesocosm. The number

of L. littorea was counted in 60 frames of a size of

5 9 5 cm within a fringe of 5 cm below the mean water

level repeatedly every fourth hour over 24 h. Only the

mean abundance per mesocosm (number given for

100 cm2) was used for the further analysis, in which four

enriched mesocosms were compared to four unenriched

Mar Biol (2012) 159:837–852 839

123

mesocosms. L. littorea biomass was estimated from the

average size of the snails in three randomly chosen frames

from each mesocosm. The size of L. littorea was measured

from the apex to the opposite point of the operculum, and

these length measurements were transferred to dry weights

using the equation by Asmus (1987):

Length (cm) ¼ 2:37þ 0:33 ln DW(g) ð1Þ

The average dwt of L. littorea was multiplied with the

average abundance to get the biomass per 100 cm2 on the

wall, and this data were analyzed by a two-way nested

ANOVA with factors nutrient (fixed, two levels) and basin

(random, four levels, nested in nutrient) and the three frames

as replicates. The cover of green macroalgae was estimated

layer by layer (so that the total cover theoretically could

exceed 100%) on the mesocosm steps, walls, bottom, and on

the wave machine, using a 40 9 40 cm grid containing 25

smaller 8 9 8 cm quadrats. Cover values were transferred to

biomass from wet weights of known surface areas of the algal

species in the mesocosms (Kraufvelin et al. 2010). The total

biomass of green algae is hereafter referred to as total

biomass of Ulva spp. (due to the dominance of Ulva lactuca

with minor contribution from Ulva intestinalis) in contrast to

the green turfs that were separately estimated inside the small

5 9 5 cm quadrats within the same fringe on the walls in

which L. littorea were estimated above. This latter data set

consists of a mixture of Cladophora spp. and Ulva

intestinalis and is referred to as green turfs on the walls.

As for L. littorea abundance above, only the total biomass of

Ulva spp. per mesocosm was used for the further analyses, in

which four enriched mesocosms were compared to four

unenriched mesocosms. For these variables, the differences

between unenriched and enriched mesocosms were analyzed

by one-way ANOVA, while differences for L. littorea

biomass were analyzed by a two-way nested ANOVA.

Before the analyses, it was checked for normality with

Kolmogorov–Smirnov’s test and homogeneity of variances

by Cochran’s test. Total biomass of Ulva spp. was

transformed by the square root, and biomass of L. littorea

was transformed by ln(x ? 1) to meet the assumptions of

parametric tests.

Determination of L. littorea ingestion rates and grazing

impacts using the gut fluorescence technique (GFT)

For the GFT-work, individual snails of L. littorea were

collected from the north-western corners of all mesocosm

walls in order to avoid disturbing L. littorea on the northern

walls, where the spatial analyses were carried out. After

this, the biomass of L. littorea was determined as described

by Asmus (1987) above. By the use of tweezers, the

organisms were immersed in chloridric acid (HCl, 7%) for

5 s in order to destroy the amount of chlorophyll-a

remaining on the shell and while taking care that the

operculum was not covered with acid. Then, the shell was

dried using paper, and the animal was crushed and

Fig. 1 Schematic view over one Solbergstrand mesocosm. Most sampling for this article took place on the northern wall to the upper right

840 Mar Biol (2012) 159:837–852

123

immediately immersed in a vial containing 8 ml methanol

(80%) for 24 h in the darkness at 4�C.Ingestion rates (I, unit lg Chl-a g ind-1 day-1) of

L. littorea were estimated using the equation of Mackas

and Bohrer (1976), which also has been used in pelagic

studies (Perissinotto 1992; Bernard and Froneman 2005):

I ¼ K � G0= 1� b0ð Þ ð2Þ

where K(h-1) is the gut depletion rate, G0 (lg g-1 ind-1) is

the integrated gut pigment, and b0 is the non-dimensionalindex of pigment destruction.

Integrated gut pigment G0 (lg g ind-1)

Three individuals of L. littorea were collected from each

mesocosm at intervals of 4 h over a 24 h period, and the

pigments were extracted as the animals were being col-

lected. One-way repeated measures (RM) ANOVA was

used to test for differences in gut contents of L. littorea

between nutrient conditions at different times of the day,

where nutrient level was the main factor and time was the

within subject factor. The assumptions of normality and

homoscedasticity were checked and if they were violated,

ln(x ? 1) transformations were used. The assumption of

sphericity was violated (Mauchly’s test), and therefore, the

P values were adjusted using the Greenhouse–Geisser

criterion (Scheiner and Gurevitch 1993).

Gut depletion rate K(h-1)

To determine the time necessary for the algal food to pass

through the gut of L. littorea, 33 individuals were collected

from each nutrient level and the pigment concentration

of three individuals at intervals of 25 min (11 intervals =

4 h and 58 min) was determined. The concentration of

chlorophyll-a was plotted versus time, and a non-linear

regression equation was calculated. The significance of the

regression was tested. The slope of the equation corre-

sponded to the rate of pigment evacuation from the gut

over time, which was compared between enriched and

unenriched mesocosms using comparison of slopes (Sokal

and Rohlf 1995).

Gut pigment destruction (b0)

In order to investigate the loss of chlorophyll-a to non-

fluorescent derivatives, a ‘‘two-compartment budget

approach’’ used for pelagic organisms was adapted. The

loss of pigment into non-fluorescent components in the

digestion process, which represented the non-dimensional

variable b0, was thereby estimated. Our modificationapplied to benthic ecology consisted of the use of ceramic

plates, 7.5 by 7.5 cm, containing a known amount of

microalgae, instead of a volume of water containing a

known concentration of phytoplankton. A total of eight

replicate ceramic plates were prepared (one per mesocosm)

and placed out for microalgal colonization during 4 months

(May to September 2008). After this period, 58 individuals

of L. littorea were removed from enriched and unenriched

mesocosms and placed into separate aquaria (24 each from

enriched and unenriched mesocosms and additionally 10

individuals were used to estimate the initial/basal content

of chlorophyll-a in their guts, five from enriched and

unenriched mesocosms, respectively). The aquaria con-

tained individual compartments for each L. littorea to

prevent the snails from feeding on the shells of each other.

The L. littorea specimens remained in isolation for 24 h

with constant air and water flow before the experiment.

At the start of the experiment, half of the ceramic panel

was cut and submerged into a petri dish containing three

L. littorea specimens, leaving them feeding for 5 h. The

other half of the ceramic panel was left submerged in

another petri dish without L. littorea. Once the feeding

period ended, the individuals were removed and their gut

contents were determined. Similarly, the concentration of

chlorophyll-a on both ceramic panels were determined. The

loss of pigment into non-fluorescent derivatives (b0) wasexpressed as percentage and estimated using the equation:

b0 ¼ ½Ct � ðGt þ PtÞ�=Ctf g � 100 ð3Þ

where Ct is the concentration of chlorophyll-a in the con-

trol panel (without L. littorea), Gt and Pt are the concen-

tration of chlorophyll-a in the guts of L. littorea specimens

and on the treatment panel at the end of the incubation,

respectively. Gut pigment destruction estimates between

mesocosms were compared using the Mann–Whitney test,

due to the non-normality nature of the data.

Analysis of spatial patchiness of L. littorea

on the mesocosm walls using semivariograms

and fractal dimension

The spatial distribution patterns generated by the behavior

of L. littorea were analyzed by counting individuals in a

fixed transect (length: 3 m) within a fringe of 5 cm below

the mean water level on the northern (sunny) walls of each

mesocosm. The transects were sampled using contiguous

quadrats of 5 9 5 cm, which allowed a sample size of 60

quadrats per transect with a minimum lag of 5 cm, defined

as the distance between centers of two adjacent quadrats.

L. littorea individuals were counted in every quadrat and

every 5 h during 24 h in every mesocosm. Additionally,

estimation of percent cover of barnacles, green algal turfs

(i.e., the mixture of Cladophora spp. and Ulva intestinalis

L.), Hildenbrandia rubra (Sommerfelt) Meneghini, and

biofilms on the wall was done by taking digital

Mar Biol (2012) 159:837–852 841

123

photographs of each quadrat, which were later analyzed

using the program Image Tool 3.0.

Spatial heterogeneity (patchiness) over time was esti-

mated using geostatistical tools, and the fractal dimension

D. Semivariogram analysis was used to determine spatial

variability and spatial dependence in the distribution of

L. littorea at different scales. The relationship between

semivariance and lag was analyzed in order to be able to

determine the spatial patterns at different times. The

semivariance (Y(h)) was estimated using the equation:

YðhÞ ¼1

2NðhÞ

XNðhÞ

i¼1ðZiþh � ZiÞ2 ð4Þ

where N is the total number of data points; N(h) is the

number of pairs of data points separated by the lag h; Zi and

Zi?h are the values of the studied variable at points i and

i ? h (Dale 2000). Fractal scaling analysis was used as an

estimation of the heterogeneity of spatial distributions over

the range of small scales (0.05–3 m). The fractal dimension

(D) was calculated from the logarithmic semivariogram

(log–log plot of semivariance and lag), using the equation:

D ¼ ð4� mÞ=2 ð5Þ

where m is the absolute slope of the regression between

semivariance and spatial lag (see e.g., Schmid 2000).

Fractal dimension is a non-integer measure of heteroge-

neity. Values of D lower than 1.5 indicate spatial trends in

the distribution, for example, environmental gradients,

while values larger than 1.5 indicate patchy distributions

(Kostylev and Erlandsson 2001). Simulations of distribu-

tions along a transect have shown that data generated

randomly produce spatial patterns with D values between

ca 1.97 and 2 (Erlandsson et al. 2005). This indicates

independence of the variance from the spatial lag (the slope

of the regression in the semivariogram is not significantly

different from 0), that is, random distribution patterns or

homogeneity (Dale 2000).

Lags up to half of the transect length were included in

the regression analysis of the semivariogram. In order to

make the analysis statistically robust, the minimal sample

size used to analyze the variance at different lags was 30.

This is because semivariances do not represent variation

between all data points at lags larger than half of the

transect length (Schmid 2000; Erlandsson and McQuaid

2004; Erlandsson et al. 2005), as at each successively lar-

ger scale, the number of comparisons decreases by one

(from 59 pairs of combinations at lag 0.05 m to 30 pairs at

lag 1.5 m).

Different fractal dimensions ‘‘D’’ can be estimated for

each scaling region, and to detect these scaling regions,

three conditions/steps need to be achieved (Kostylev and

Erlandsson 2001): (1) detection of scaling breaks using

residual analysis, (2) significant linear regression of the

suggested scaling sub-relationship, and (3) significant dif-

ference between the slopes of successive scaling regions

(see Kostylev and Erlandsson 2001; Erlandsson and

McQuaid 2004; Erlandsson et al. 2005 for more details).

Sequential table-wise Bonferroni tests (Hochberg 1988)

were applied for all the regression analyses to adjust the P

values into accordance with the number of tests performed.

Analysis of spatial co-variation between

L. littorea and community components

using cross-semivariograms

In order to describe the relationship between the spatial

patterns of L. littorea and spatial patterns of barnacles and

algae across different spatial scales (from 0.05 to 1.5 m

lags), cross-semivariogram analysis was used. The cross-

semivariance was estimated by the equation:

YxzðhÞ ¼1

2NðhÞ

XNðhÞ

i¼1ðXiþh � XiÞðZiþh � ZiÞ ð6Þ

where N is the total number of data points; N(h) is the

number of pairs of data points separated by the distance or

lag h; Xi and Xi?h, and Zi and Zi?h are the values of two

different variables (e.g., density of L. Littorea and barnacle

cover, respectively) at points i and i ? h (Dale 2000;

Erlandsson and McQuaid 2004; Erlandsson et al. 2005).

The studied community components were as follows:

barnacles, biofilm, green algal turfs, and H. rubra.

A positive or a negative cross-semivariance value at a

certain lag indicates a positive or a negative co-variation,

respectively, at that scale. A cross-semivariance value

approaching zero indicates no co-variation between vari-

ables at that scale. To test whether cross-semivariance

values were significantly different from 0, the distributions

of pairs of variables along each transect were randomized

1,000 times and cross-semivariance was calculated at each

scale for each random permutation. Each randomized value

was compared with the appropriately observed cross-

semivariance value. Then, the probability of each observed

cross-semivariance value being higher (positive relation-

ship) or lower (negative relationship) than by chance alone

was calculated, and an alpha level of 0.05 was applied. The

analyses were carried out using Matlab 7.0.1.

The significant co-variation detected was categorized

into three groups of spatial scales: (1) microscales com-

prising lags from 5 to 50 cm, (2) mesoscales comprising

lags from 50 to 100 cm, and (3) macroscales comprising

lags between 105–150 cm. The microscale has been

defined as the scale where the organisms interact, for

example, L. littorea intraspecifically, interspecifically, and

with their food item (Underwood and Chapman 1996).

842 Mar Biol (2012) 159:837–852

123

The mesoscale is the scale where the assemblage can reveal

a patchy structure. The macroscale represents up to half of

the total length of the transect. The frequencies of signifi-

cant co-variation among the three groups of scales were

compared using goodness of fit.

Results

Background nutrient concentrations, abundance,

and biomass of L. littorea and total biomass

of Ulva spp.

Background nutrient levels in the Oslofjord were quite high

during the experimental period or around 0.45 lM P and17 lM N (measurements from Norwegian Institute forWater Research from surface water in May and in August

2008). Since the nutrient dosing worked perfectly

throughout the experimental period, this meant that enri-

ched mesocosms on average had 5.4 times higher P levels

and 2.9 times higher N levels than unenriched mesocosms.

A number of significant differences between the enri-

ched and the unenriched mesocosms had occurred after

5 months of experimentation, and among the ones of direct

relevance for this study, both the abundance and biomass of

L. littorea on the northern wall as well as the total biomass

of green Ulva spp. in the mesocosms were stimulated by

nutrient enrichment. There was almost 60% higher abun-

dance (F1,6 = 6.01, P \ 0.05) (Fig. 2a) and almost 100%higher biomass (F1,6 = 13.67, P \ 0.01) (Fig. 2b) ofL. littorea on the walls of enriched systems compared to

the unenriched ones in September, despite the original

numbers and biomass of adult L. littorea being equal in the

mesocosms in April (data not shown). The total biomass of

Ulva spp. was in September seven times higher in enriched

mesocosms than in unenriched mesocosms (F1,6 = 17.07,

P \ 0.01, Fig. 2c), despite equal levels in April.

L. littorea ingestion rates and grazing impacts

using GFT

The gut depletion rate (K) was higher (steeper slope in the

regression) in L. littorea in unenriched than in enriched

mesocosms (slope test: F4,15 = 6.6, P \ 0.05; Table 1,Fig. 3), while the integrated gut pigment (G0) was higher in

L. littorea from enriched than from unenriched mesocosms

(F1,22 = 7.9, P \ 0.01; Table 1, Fig. 4). There were alsodifferences in the integrated gut pigment between times of

the day (F6,132 = 6.09, P \ 0.001) in such a way that G0tended to be higher in the evening/night at 20.00 and 00.00

than in the morning/day at 04.00, 08.00 and 12.00. There was

no interaction between nutrient input and time of the day

(Fig. 4). No significant differences were found in ingestion

rates (I) and gut pigment destruction rates (b0) for L. littoreabetween enriched and unenriched mesocosms (Table 1).

Spatial patterns of L. littorea using semivariograms

Spatial structure/heterogeneity in the distribution of L. lit-

torea (dependence between variability in the number of

L. littorea and lag), that is, indicating clumping (not

feeding), was often observed in the morning (ca half of all

Ulv

asp

p. b

iom

ass

g w

wt

0

100

200

300

400

500

600

700

800

900

1000

UnenrichedEnriched

B

A

Lit

tori

na

abu

nd

ance

per

100

cm

2

0

1

2

3

4

5

6

7

8

9

UnenrichedEnriched

B

AA

B

C

Enriched

Lit

tori

na

bio

mas

s g

dw

per

100

cm

2

Unenriched

A

B

0

0,5

1

1,5

2

2,5

3

3,5

Fig. 2 a Average abundance b Average biomass of L. littorea ? SDper 100 cm-2 on the walls in enriched and unenriched mesocosms.

c Total biomass in g wwt of Ulva spp. ?SD in enriched andunenriched mesocosms. Significant differences are denoted by lettersabove the bars

Mar Biol (2012) 159:837–852 843

123

morning transects) both in nutrient-enriched and nutrient-

unenriched mesocosms (regressions significant, D \ 1.97,Table 2). Most other distributions of L. littorea in the noon,

evening, and at midnight (both treatments; 21 of 24 tran-

sects) showed spatial independence (random patterns, non-

significant regressions) indicating feeding and, overall,

very few transects showed significant multiple scaling

regions (Table 2). The spatial structure observed was

always a patchy/aggregated distribution.

Spatial relationship between L. littorea and community

components using cross-semivariograms

The distribution of different community components (bar-

nacles, biofilm, green algal turfs, H. rubra) on the meso-

cosm walls can be seen in Fig. 5 as background data to the

investigation of co-variation between L. littorea and com-

munity components. Among these, the higher amount of

green turfs in unenriched mesocosms is an unexpected

result, which should not be mixed up with the total biomass

of green Ulva spp. in the mesocosms, which was higher in

the enriched mesocosms (Fig. 2b). The relationships

between spatial variability of L. littorea distributions and

the community components did not vary much over 24 h.

However, differences in the sign of the spatial co-variation

were detected for some relationships:

Spatial co-variation between barnacles and L. littorea

Significant negative spatial co-variation between L. littorea

and barnacles dominated in the unenriched mesocosms, but

not in the enriched mesocosms. Most of the significant neg-

ative co-variation were at the largest lags 105–150 cm,

(v2 = 27.12, P \ 0.001 in unenriched mesocosms). Therewas only one significant positive spatial relationship between

L. littorea and barnacles in unenriched mesocosms. In con-

trast, there were more significant positive relationships

in enriched mesocosms distributed equally among micro-,

meso-, and macro scales (v2 = 2.71, P = 0.25) (Fig. 6a).

Spatial co-variation between biofilm and L. littorea

The co-variation between biofilm and L. littorea exhibited

predominantly positive relationships at both nutrient levels

in terms of the number of significant lags found. Most

positive co-variation was found at meso- and macro lags

Table 1 Variables obtained to determine daily ingestion rates ofL. littorea in nutrient-enriched and nutrient-unenriched meso-cosms ± SD: (1) integrated gut pigment (G0), (2) gut depletion rate

(K) (K does not have a SD because it was calculated from the slope ofgut content versus time, see methods), (3) gut pigment destruction

(b0), (4) daily ingestion rate

Treatment Integrated gut pigment, G0(P

lg Chl-a. g-ind-1)K(h-1) b0 Daily ingestion rate

(I, lg Chl-a. g-ind-1 day-1)

Enriched mesocosms 66.78 0.264 0.36 ± 0.4 24.78 ± 2.8

Unenriched mesocosms 37.54 0.381 0.14 ± 0.2 16.88 ± 9.7

Fig. 3 Non-linear regressionsof gut depletion rate (time in h)

versus chlorophyll-a content inenriched systems (blackdiamonds; R2 = 0.44;y = 7.52e-0.264x) and inunenriched systems (opensquares; R2 = 0.34;y = 2.48e-0.382x)

Time (hours)

µg C

hla.

gin

d-1

02468

101214161820

20 pm 0 am 04 am 08 am 12 pm 16 pm 20 pm

Enriched mesocosmsUnenriched mesocosms

Fig. 4 Integrated gut pigment for L. littorea. Average in the amount ofchlorophyll-a (?SD) contained in the gut of individuals that wereinhabiting enriched and unenriched mesocosms at each time during 24 h

844 Mar Biol (2012) 159:837–852

123

Table 2 Regression exponents of the logarithmic semivariograms, and fractal dimensions (D) for the spatial lags of L. littorea distribution in thedifferent mesocosms and transects: (a) unenriched and (b) enriched mesocosms

Mesocosm and Time Lags (m) Slope R2 P Fractaldimension (D)

Spatial pattern

a. Unenriched

1: 7 am 1 0.05–1.5 0.17 0.57 0.000001 1.917 Dependence—patchy

1: 12 am 0.05–1.5 0.15 0.61 0 1.925 Dependence—patchy

1: 18 pm 0.05–1.5 0.04 0.06 ns 1.979 Independence—random


1: 7 am 2 0.05–1.5 -0.03 0.02 ns 1.984 Independence—random

2: 7 am 1 0.05–1.5 0.08 0.14 0.045 1.962 Patchy

2: 12 am 0.05–1.5 0.11 0.40 0.0002 1.944a Dependence—patchya

Multiple scaling regions 0.05–1.05 0.12 0.46 0.0007 1.940 Dependence—patchy

1.10–1.5 -0.91 0.51 0.03 1.545 Patchy

2: 18 pm 0.05–1.5 -0.03 0.02 ns 1.984 Independence—random



3: 7 am 1 0.05–1.5 0.07 0.25 0.0045 1.964 Patchy

3: 12 am 0.05–1.5 -0.03 0.04 ns 1.984 Independence—random





4: 12 am 0.05–1.5 0.11 0.27 0.0031 1.947 Patchy


4: 00 pm 0.05–1.5 0.11 0.19 0.015 1.947 Patchy


Total 7 am 1 2–4 transects show spatial structure

Total 12 am 2–3 transects show spatial structure

Total 18 pm 0 transects show spatial structure

Total 00 pm 0–1 transect shows spatial structure

Total 7 am 2 1 transect shows spatial structure

b. Enriched

1: 7am 1 0.05–1.5 0.05 0.08 ns 1.973b Independence—randomb

Multiple scaling regions 0.05–0.6 0.19 0.66 0.0013 1.905 Dependence—patchy

0.65–1.5 -0.31 0.29 0.022 1.845 Patchy

1: 12 am 0.05–1.5 0.04 0.10 ns 1.978 Independence—random



1: 7 am 2 0.05–1.5 0.1 0.27 0.0034 1.951 Patchy

2: 7 am 1 0.05–1.5 0.07 0.11 ns 1.967 Independence—random



2: 00 pm 0.05–1.5 0.06 0.13 0.049 1.969 Patchy

2: 7 am 2 0.05–1.5 0.01 0.01 ns 1.993 Independence—random


3: 12 am 0.05–1.5 -0.05 0.12 ns 1.974 Independence—random





Mar Biol (2012) 159:837–852 845

123

(enriched: v2 = 7, df = 2, P \ 0.05, and unenriched:v2 = 59.27, df = 2, P \ 0.001). Significant negative co-variation was scarcely present in unenriched mesocosms, but

more abundant in enriched mesocosms. This negative co-

variation was distributed evenly through the micro-, meso-,

and macrolags at both nutrient levels (v2 = 6, df = 2,P [ 0.05 and v2 = 5.8, df = 2, P [ 0.05) (Fig. 6b).

Spatial co-variation between H. rubra and L. littorea

On the mesocosm walls, H. rubra was only present in

one enriched mesocosm. Here, the relationship between

H. rubra and L. littorea was only negative, which was

significant at meso- and macrolags (v2 = 34.86, df = 2,P \ 0.001) (Fig. 7a).

Spatial co-variation between green algal turfs

and L. littorea

Along the analyzed wall transect, only two unenriched

mesocosms exhibited green turfs. The spatial co-variation

was predominantly negative, but there were also a few

positive relationships. While the negative relationships were

distributed evenly through the lags (v2 = 2.33, df = 2,P [ 0.05), the positive co-variation was more abundant atmacrolags (v2 = 12, df = 2, P \ 0.01) (Fig. 7b).

Discussion

Mostly similar overall responses to nutrient enrichment as

in previous Solbergstrand mesocosm experiments were

found in macroalgal and macrofaunal community structure,

and this was also true for the stimulation of L. littorea and

total biomass of Ulva spp. (Fig. 2a,b,c), supporting

hypothesis 1, but the occurrence of green algal turfs only

on the walls of two unenriched mesocosms was an

exception (Fig. 5). A higher total abundance of L. littorea

in nutrient-enriched systems was also found by Kraufvelin

et al. (2002) and higher total biomass of Ulva spp. in

enriched systems by, for example, Bokn et al. (2003),

Karez et al. (2004), Kraufvelin (2007), Kraufvelin et al.

(2006b, 2010). For L. littorea, the abundance stimulation

was probably due to a much higher recruitment (higher

nativity, higher survival, lower mortality) in the enriched

systems during summer, since most individuals present in

September were juveniles. In addition, the average size of

L. littorea was higher in enriched systems causing a sig-

nificantly higher biomass of the grazer and revealing that

also the growth of the juveniles had been enhanced. These

results for L. littorea thus reflected processes that were

taking place in the mesocosms during the 5 months the

experiments lasted and that were under the influence of the

nutrient treatment, such as a stimulation of total biomass of

Ulva spp. (Fig. 2c). This probably implied increased food

availability, increased food nutrient richness, and more

0

20

40

60

80

100

120

green turf barnacles biofilm Hildenbrandia

per

cen

tag

e o

f cov

er

Enriched mesocosms

Unenriched mesocosms

community components

Fig. 5 Mean ? SD of the cover of community components on thenorthern walls of the mesocosms

Table 2 continued

Mesocosm and Time Lags (m) Slope R2 P Fractaldimension (D)

Spatial pattern



4: 00 pm 0.05–1.5 0.13 0.31 0.0013 1.936 Dependence—patchy



Total 12 am 0 transects show spatial structure

Total 18 pm 0 transects show spatial structure

Total 00 pm 1–2 transect shows spatial structure


Significant P values after a sequential Bonferroni correction are in bold facea Scaling break at the lag 1.05 mb Scaling break at the lag 0.6 m

846 Mar Biol (2012) 159:837–852

123

favorable species composition of the food for L. littorea,

as previously also was demonstrated for G. locusta by

Kraufvelin et al. (2006a). Apparently, these initial effects

of nutrient enrichment, like a stimulation of fast-growing

green algae and certain grazers, seem to be quite universal

and to take place also in the absence of tides and at lower

light intensity. These results are also in agreement with

corresponding findings from field investigations on natural

temperate shores (e.g., Worm and Lotze 2006; Eriksson

et al. 2006, 2009; Masterson et al. 2008). The lack of green

turfs on the walls of enriched systems may be due to

several reasons, among others a higher abundance and

biomass of grazing L. littorea, since green turfs are among

the preferred food items for this species and could thus be

rapidly grazed away by L. littorea (Wilhelmsen and Reise

1994) or with the help of other dominant grazers such as

G. locusta (Kraufvelin et al. 2006a). Hence, from a whole-

mesocosm perspective, the grazers do not seem to be able

to control total biomass of Ulva spp., but it seems that, at

least on the walls, the amount of green algal turfs is grazer

mediated.

The present study shows that the gut fluorescence

technique (GFT) also works for benthic grazers such as

L. littorea (Fig. 3), and the preferred feeding on green

filamentous and sheet-like algae, rich in chlorophyll-a

pigments, makes this grazer appropriate for the technique.

One of the main criticisms of the technique is that it pro-

vides only a measure of the herbivorous activity of an

organism and fails to consider the possibility that organ-

isms are consuming alternative carbon sources, including

A B

Fig. 6 Number of significant lags that exhibited statistical signifi-cance in the spatial co-variation between L. littorea and an overallpresent community component. a Spatial relationship betweenL. littorea and barnacles. This analysis showed positive spatialco-variation only in enriched mesocosms, while negative spatial

co-variation was predominant in unenriched mesocosms. b Spatialrelationship between L. littorea and biofilms. This showed adominance of positive spatial co-variation in both enriched and

unenriched mesocosms

A B

Fig. 7 Number of significant lags that exhibited statistical signifi-cance in the spatial co-variation between L. littorea and a communitycomponent that was not present in all mesocosms. a Significantnegative spatial co-variation between L. littorea and Hildenbrandia sp

was present in the enriched mesocosm where the algae occurred.

b Significant spatial co-variation between L. littorea and green turfs inthe two unenriched mesocosms was mainly negative

Mar Biol (2012) 159:837–852 847

123

detritus or heterotrophic carbon sources (Boyd et al. 1980;

Båmstedt et al. 2000). However, many rocky shore gas-

tropods, such as L. Littorea, are predominantly herbivo-

rous, feeding mainly on the thin film of epiphytic algae or

diatoms, on green algae as well as on brown algal germ-

lings (Wilhelmsen and Reise 1994), which means that the

GFT may be very suitable to estimate the grazing activity

in these benthic animals.

Pakhomov and Froneman (2004) suggested that inges-

tion rates of pelagic animals can either be affected by food

availability or by changes in feeding behavior related to

seasonal variation. Although the daily ingestion rate was

not significantly different between L. littorea-inhabiting

enriched and unenriched mesocosms, the integrated gut

pigment (G0), indicating how much algal pigment (food) is

contained in the gut in 24 h, showed lower values at low

nutrient levels, whereas the gut depletion rate (K) was

faster in L. littorea-inhabiting unenriched mesocosms. This

suggests that L. littorea consumes and retains more food in

enriched environments and that the depletion rate and

integrated gut pigment were sensitive to food availability.

However, the increased snail density and biomass under

enriched conditions may have enhanced competition for

food leading to a situation, where snails were similarly

resource limited irrespective of enrichment level. In addi-

tion, it is also possible that these results largely reflect both

qualitative and quantitative effects within the algae

(Jaschinski and Sommer 2011). In this sense, see also

Kraufvelin et al. (2006a), where a path analysis showed

that indirect effects on G. locusta density from nutrients via

green algae were 50% bigger than direct nutrient treatment

effects on gammarid abundance.

The reasons why the pigment destruction variable did not

differ between L. littorea-inhabiting environments with

different nutrient levels and that the variability was so great

within the estimations are not easy to determine. The vari-

ability could have been caused by (1) variability in the

amount of algae consumed by individual L. littorea due to

the experimental condition (experimental stress), and/or (2)

spatial heterogeneity in the abundance, species composition,

and nutrient content of algae colonizing the ceramic plates.

However, the results suggest that food availability does not

affect the ingestion rate of L. littorea. Recently, Durbin and

Campbell (2007) argued that pigment destruction should not

be estimated to calculate the daily ingestion rate, since

assimilation and destruction of pigments in the gut passage

(b0) are already estimated and present in the calculation ofgut depletion rate. Under this view, recalculating the values,

non-significant differences in the daily ingestion rates of

L. littorea between enriched and unenriched conditions were

still observed (15.9 ± 2 and 9.8 ± 8.3 lg Chl-a g ind-1

day-1, respectively) and there was still a high feeding vari-

ability within unenriched mesocosms.

Nevertheless, the opposite magnitudes in G0 and K

between nutrient levels and no differences in ingestion rate

are in agreement with the premises of Optimal Foraging

Theory, which argue that animals should be capable of

adjusting gut passage time depending on both food avail-

ability (Taghon 1981; Penry and Jumars 1986) and/or quality

of ingested food (Pakhomov and Froneman 2004). It may

therefore be concluded that the response of L. littorea to high

food availability is to slow down the gut depletion rate

(K) and the reverse at lower food availability.

At both nutrient levels, spatial heterogeneity in L. lit-

torea could be found (especially in the morning), although

random distribution patterns dominated (Table 2), indi-

cating that there may be certain times when snails are

clumped (e.g., resting) and other times when they are

dispersed (e.g., feeding) during the day/night cycle. There

has been a considerable debate about when intertidal spe-

cies are feeding and about the relationship between feeding

and the tidal regime and day/night periods (Hawkins and

Hartnoll 1983; Little et al. 1991; McQuaid 1996; Chapman

2000). Interestingly, with continued experimentation using

replicated days and nights, the applied techniques would

have allowed us to formally test when these grazers were

actually consuming algae through the evaluation of the gut

contents (integrated gut pigment variable, G0) over time

and to relate these values to their spatial distribution. In the

present experiment, the results for G0 show that the con-

centrations of algal pigments in the guts of L. littorea

varied between different times over the studied 24 h and

that some of these values seemed to fit with their spatial

distribution patterns (hypothesis 2 partly confirmed, Fig. 4,

Table 2). It has been suggested that intertidal grazers such

as L. littorea mainly feed during the night as an adaptive

response to avoid visual predators (Carlson et al. 2006) and

desiccation (Newell et al. 1971; Chapman and Underwood

1996). Nevertheless, the variability in the activity of

L. littorea was great with some individuals being found in

patches and some dispersed, regardless of nutrient condi-

tion and time of the day.

As our sampling was carried out on homogeneous sur-

faces without crevices (on concrete walls), our study shows

that clumping behavior can be determined by other biotic

factors than, for example, crevices and shelter on the rock

surface (e.g., Underwood and Chapman 1996; Erlandsson

et al. in prep), such as the comprising community (Fig. 5),

especially the barnacles, although we do not want to

underestimate the potential effect of the complexity of the

substratum (Skov et al. 2010). Here, the space in between

barnacles and mussels can thus be important for the

abundance of Littorina sp. depending on the size of the

littorinid species or morph/ecotype (Kostylev et al. 1997).

Furthermore, the spatial relationship of L. littorea and

barnacles in the present study did not change during the

848 Mar Biol (2012) 159:837–852

123

day, but it differed with regard to nutrient levels (Fig. 6a).

Thus, L. littorea from enriched mesocosms preferred to

inhabit spots where barnacles were present (mainly posi-

tive co-variation), while L. littorea from unenriched mes-

ocosms avoided patches with barnacles (mainly negative

co-variation) suggesting that the nutrient level of the sys-

tem may drive this relationship and providing support for

hypothesis 3. However, earlier or unpublished field studies

indicate that barnacle cover affects the distribution of

L. littorea negatively, just as could be seen in the unen-

riched mesocosms (Fig. 6a), while rough periwinkles (e.g.,

Littorina saxatilis Olivi and Littorina arcana Hannaford-

Ellis) are affected positively by barnacles (Kostylev et al.

1997; Erlandsson et al. in prep.), and in Littorina sitkana

Philippi, the size of the snail also affects this association

(Jones and Boulding 1999). In our study, the clearly dif-

ferent L. littorea preferences for barnacles at the different

nutrient levels are not easily explained, but one reason may

be that L. littorea in the enriched mesocosms, due to higher

competition and less green turfs on the walls, also had to

feed on epiphytes on the barnacles, which was not the case

in the unenriched mesocosms. Indications that L. littorea

could be capable of feeding on barnacle larvae would

further complicate these interactions (Wahl and Sönnich-

sen 1992; Buschbaum 2000) and may be another reason for

the positive co-variation between L. littorea and barnacles

found in the enriched systems. On the other hand, the

differences in spatial co-variation between L. littorea and

barnacles in enriched and unenriched mesocosms can also

be due to the higher abundance of L. littorea in the enri-

ched mesocosms showing positive co-variation with bar-

nacles regardless of its dispersion.

With regard to other interactions with community

components, that is, biofilm, green algal turfs, and H. rubra

on the wall, some additional interesting findings were made

(Figs. 6b, 7a,b). The co-variation between L. littorea and

biofilms also differed between enriched and unenriched

mesocosms, being only positive in unenriched mesocosms

and both positive and negative in enriched mesocosms,

suggesting that feeding on biofilms, which is in agreement

with their expected diet (Norton et al. 1990; Hillebrand

et al. 2000; Skov et al. 2010), was more important in

unenriched environments. This is slightly in contrast to the

situation for L. littorea co-variation with barnacles above

but may be due to complex preference patterns among the

snails such as differences in reactions to nutrient enrich-

ment levels for the various potential food resources, that is,

green turfs, biofilm, epiphytes on barnacles, etc. (Karez

et al. 2004; Kraufvelin et al. 2006a). Some differences in

community structure between mesocosms may also have

been caused by the higher abundance and biomass of

L. littorea, and/or thereby higher grazing rates in the enri-

ched mesocosms. An increased grazing pressure and

decreased amount of green turfs on the walls may have

promoted the domination of biofilms and eventually the

presence of the encrusting alga Hildenbrandia sp. (Bertness

et al. 1983). H. rubra was observed in one out of four

enriched mesocosms, while this species was absent from

unenriched mesocosms. It has been reported that Hilden-

brandia sp. uses antifouling chemical defense to inhibit

settlement of foliose algae and microalgae (Madikiza 2005).

This could cause the inhibition of food searching in L. lit-

torea. The lack of H. rubra seemed to be compensated for

by the presence of green algal turfs on the walls of unen-

riched mesocosms. A general increase in the primary pro-

ductivity in enriched mesocosms could also in itself have

facilitated the development of macroalgae that rapidly were

consumed by grazers (Kraufvelin et al. 2002, 2006a), in turn

promoting the cover of biofilms. In unenriched mesocosms,

on the other hand, some spots of green algal turfs on the

walls could sustain the low abundance of grazers.

The realism of mesocosm studies may always be ques-

tioned; see for example Perez (1995) and Kraufvelin

(1999) regarding mesocosms in general and Kraufvelin

et al. (2006b, 2010) regarding the Solbergstrand meso-

cosms specifically. Nevertheless, with regard to this study,

there were a number of undisputable advantages with using

the mesocosm approach compared to visiting many dif-

ferent field sites. Among these, there were controlled

nutrient levels and equal substrate material, topography,

wave action (both wave height and direction), water cur-

rents, water temperatures, light conditions (both intensity

and timing), and predator abundance (constantly low).

Most importantly for this study, there were four replicated

‘‘shores’’ of each nutrient level available within a few

meters and these shores/mesocosms could be accessed by

the same researchers within a few seconds enabling repe-

ated ‘‘simultaneous’’ sampling. A similar study could not

have been done in the field by the same amount of

resources and man-power. In that sense, the possible

restrictions imposed by the mesocosm enclosure, for

example, lower predator levels and thereby a possibly

altered gastropod behavior (see Coleman et al. 2006;

Coleman 2010) and lower wave exposure and thereby

lowered dilution of nutrients (see Valdivia and Thiel 2006),

should not be more serious than site to site differences

(context dependency) out in the field (Burkepile and Hay

2006; Connell and Irwing 2009; Wahl et al. 2011; Bulleri

et al. unpubl.).

To summarize, this study offers insights into feeding

behavior and spatial distribution of L. littorea and how the

species interacts with community components through the

consumption of certain algal groups and then promotion of

the recruitment of other components in the community

differently under different nutrient regimes (possible

interaction between top-down and bottom-up effects).

Mar Biol (2012) 159:837–852 849

123

The combination of the GFT and spatial statistical tech-

niques introduces a new approach in the examination of

small-scale distribution and feeding activities of L. littorea

under nutrient-enriched and nutrient-unenriched condi-

tions. All hypotheses were supported to various degrees,

and among the most important results, the biomass of

L. littorea and Ulva spp. showed clear positive responses to

the nutrient addition, the integrated gut pigment was higher

in L. littorea from nutrient-enriched mesocosms, and dif-

ferent nutrient conditions showed contrasting co-variation

between L. littorea and barnacles. The applied methodol-

ogy can be useful for purposes of faster examination of

grazing effects in communities separated geographically

and also to compare grazing intensities and interactions

between grazers and the rocky shore communities in dis-

turbed (including pollution and nutrient enrichment) and

non-disturbed systems, as well as in up-welling versus non-

upwelling systems.

Acknowledgments ED and JE were funded by Formas (TheSwedish Research Council for Environment, Agricultural Sciences

and Spatial Planning), whereas PK was funded by Svenska Kultur-

fonden. We are grateful to Hartvig Christie and Sofie Knutar for field

assistance, to Per-Ivar Johannessen and Oddbjørn Pettersen for

excellent daily maintenance of the Solbergstrand mesocosms, and

to William Froneman and Kim Bernard for insights about the use

of GFT. Hartvig Christie’s and Frithjof Moy’s scientific project

SACCHARINA (from the Research Council of Norway 2007–2010)covered the costs for operative mesocosms during 2008 and their kind

approval of our simultaneous research activities in the systems

is highly appreciated. The Solbergstrand mesocosms can be viewed

live at the web-cam link: http://151.157.160.150/view/index.shtml

(username and password = guest).

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Combining gut fluorescence technique and spatial analysis to determine Littorina littorea grazing dynamics in nutrient-enriched and nutrient-unenriched littoral mesocosmsAbstractIntroductionMaterials and methodsSolbergstrand rocky littoral mesocosmsDetermination of abundance and biomass of L. littorea and green algaeDetermination of L. littorea ingestion rates and grazing impacts using the gut fluorescence technique (GFT)Integrated gut pigment G0 (microg g indminus1)Gut depletion rate K(hminus1)Gut pigment destruction ( b^{\prime } )

Analysis of spatial patchiness of L. littorea on the mesocosm walls using semivariograms and fractal dimensionAnalysis of spatial co-variation between L. littorea and community components using cross-semivariograms

ResultsBackground nutrient concentrations, abundance, and biomass of L. littorea and total biomass of Ulva spp.L. littorea ingestion rates and grazing impacts using GFTSpatial patterns of L. littorea using semivariogramsSpatial relationship between L. littorea and community components using cross-semivariogramsSpatial co-variation between barnacles and L. littoreaSpatial co-variation between biofilm and L. littoreaSpatial co-variation between H. rubra and L. littoreaSpatial co-variation between green algal turfs and L. littorea

DiscussionAcknowledgmentsReferences

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