Top-down controls and trophic cascades in estuarine...

Top-down controls and trophic

cascades in estuarine intertidal

sediments: the role of predatory

nematodes

Sara Albuixech Martí

Academic year 2013 – 2014

Promoter: Prof. Tom Moens

Supervisors: Xiuquin Wu; Tania

Campiñas Bezerra

Ghent University

Faculty of Science

Department of Biology

Thesis submitted to obtain the degree

of European Master of Science in

Nematology

1

The trophic web, which interconnects the different living and non-living

compartments in an ecosystem via the transfer of matter and energy, is a fundamental

concept in ecology. The trophic web is characterized by multiple and complex

interactions between different levels (Van der Meer et al., 2005). Consequently its study

is difficult, and reductionist lab experiments and simulations which simplify the natural

system are essential for a better understanding of the different processes and interactions

that govern food web structure and dynamics (O'Gorman et al., 2008).

O'Gorman et al. (2008) observed that small changes in ecosystem complexity,

such as a single species addition or deletion, may propagate throughout the whole food

web. This phenomenon is called a trophic cascade, whereby for instance a change in a

predator’s abundance not only affects the population of its prey, but also those of the

prey’s prey (Gamfeldt et al., 2005; O'Gorman et al., 2008). Trophic cascades have been

widely studied in many ecosystems (Posey et al., 1995; Shurin et al., 2002; Gruner et

al., 2008; Baum & Worm, 2009), including marine systems. It has been reported that

the loss of diversity at top trophic levels can alter the classic trophic pyramid of marine

food webs and change it into a compressed version, the consequences of which are

largely unknown (O'Gorman et al., 2008). It is well known that changes in marine

predator abundance may promote far-reaching effects for ecosystem structure,

functioning, and resilience (Baum & Worm, 2009). Such cascading effects of changing

top predator density or diversity on functioning of marine ecosystems are particularly

important for their conservation and economic value (O'Gorman et al., 2008; Baum &

Worm, 2009). Trophic cascades have been less well studied in benthic systems, and

have often been less focused on the lower trophic levels. Therefore, a better knowledge

of cascades to lower trophic levels is fundamental (Baum & Worm, 2009).

In our study we have focused on nematodes, because they are the most abundant

and diverse meiofaunal taxon in marine, freshwater and terrestrial ecosystems. In terms

of abundance, an estimated three quarters, or more, of all animals on earth are

nematodes. The number of species which have actually been properly described is

limited (ca. 30,000 including all parasitic taxa). A substantial part of that diversity and

abundance is present in marine habitats. As a taxon, free-living nematodes consume a

wide range of food sources and use a variety of feeding strategies (Yeates et al., 2009;

Moens et al., 2013). Many species presumably feed on bacteria (Moens & Vincx,

1997). They are able to influence bacterial activity, either stimulatory or inhibitory (De

2

Mesel et al., 2003). Nematode effects on bacteria may be direct, i.e. through grazing. A

moderate grazing pressure by benthic invertebrates, mainly nematodes and protists, may

be directly positive for bacteria in the sediment by keeping bacterial populations in a

prolonged state of logarithmic growth (Ingham et al., 1985; Traunspurger et al., 1997).

Furthermore nematodes may indirectly stimulate the bacterial activity in the sediment

by (1) enhanced recycling of nutrients, particularly of N (Ingham et al., 1985;

Kristensen, 1988), (2) converting particulate organic matter to dissolved organic matter

(Kristensen, 1988), (3) stimulating the upward transport of reduced and the downward

transport of oxidized compounds (Kristensen, 1988), (4) secreting mucus trails which

selectively stimulates growth of certain bacteria (Riemann & Schrage, 1978; Moens et

al., 2005), (5) transporting bacterial cells, either internally or externally on the cuticle,

to unexploited microsites (Ingham et al., 1985). Consequently, the nematode activity

may have an important influence on decomposition rates and facilitate nutrient

exchange, which plays an important role in the functioning of many ecosystems

(Traunspurger et al., 1997; De Mesel et al., 2004). Nevertheless inhibitory effects on

bacteria population have also been recorded in the presence of nematodes. De Mesel et

al. (2003) studied the influence of bacterivorous nematodes on the decomposition of

cordgrass and observed that the decomposition and nutrient mineralization slowed down

in the presence of nematodes. Bacterivorous nematodes appeared to inhibit the fungial

growth and in turn fungi are important in the early stages of decomposition of

cordgrass, hence this could be a reason for the initially slower decomposition process in

the presence of nematodes (De Mesel et al., 2003).

It is also well known that meiofauna plays an important role as a trophic link

between bacteria and larger fauna (Coull, 1999). Based on long-term variability in

meio- and macrofaunal abundance, it has been suggested that predation is the main

direct interaction that affects meiofaunal communities (Coull, 1986; Coull, 1999).

Indeed, many studies of predator-prey interactions have been performed in aquatic

communities using macrofauna as predators and meiofauna as prey (Olafsson, 2003;

O'Gorman et al., 2008). However, information about meiobenthic predators controlling

other meiofauna and/or their prey is comparatively scarce (Moens et al., 1999; Moens et

al., 2000; Hamels et al., 2001; Gallucci et al., 2005; dos Santos & Moens, 2011),

despite the suggestion that even at relatively low abundance, predatory nematodes can

substantially impact abundance, species composition and/or diversity of their prey

3

assemblages (Moens et al., 2000; Gallucci et al., 2005), i.e. the predation by nematodes

may be a driving force on community structure (Gallucci et al., 2005; Yeates et al.,

2009; Moens et al., 2013).

If predatory nematodes influence their prey assemblages, they may also affect

the abundance and activity of their prey’s prey, i.e. microphytobenthos and bacteria, and

hence ultimately influence ecosystem functioning (O'Gorman et al., 2008). Such trophic

cascades occur in many ecosystems, but have not hitherto been properly investigated for

predatory nematodes, exception made for one study by Mikola & Setala (1998) in soil

decomposer food webs. These authors performed a microcosm experiment containing

different species of bacteria and fungi as the first trophic level, a bacterivorous

nematode and a fungivorous nematode species as the second level, and a predatory

nematode species as the third level. Although the experiment did not provide evidence

that predation regulated the microbial biomass and productivity, the authors

demonstrated that microbivore biomass and mineralization of carbon and nitrogen were

influenced by the predators.

Hence, microcosm experiments are a powerful tool to evaluate top-down and

bottom-up control in benthic communities (Moens et al., 2000; Worm et al., 2002;

Gallucci et al., 2005; De Mesel et al., 2006; dos Santos & Moens, 2011). We thus used

laboratory microcosm experiments under controlled conditions to study top-down

effects in the meio- and microbenthic compartments of estuarine intertidal food webs.

Our aim was to determine the effects of bacterivorous nematodes on bacteria and how

these are influenced by predacious nematodes.

We propose four fundamental hypotheses: (1) we expect that bacterivorous

nematodes affect the microbial activity in fairly short-term microscosm experiments; (2)

predators cause a direct decrease in the abundance of their prey; (3) predation effects on

prey nematode assemblages cascade down to bacteria activity by alleviating the effects

of their grazers (bacterivorous nematodes); and (4) we hypothesize that top-down

effects are more pronounced at relatively low microbial activity.

4

MATERIAL AND METHODS

Sampling site:

Sediments, bacteria and predatory nematodes for the present experiments were

collected at the Paulina intertidal flat, Westerschelde Estuary, in SW Netherlands

(Figure 1). Sandy intertidal marine sediment (upper 1 to 2 cm) was sampled during low

tide at a medium-grained station with low silt fraction. The sampling was performed

during spring and early summer 2014.

Figure 1: Location of the sampling stations in the Westerschelde estuary, SW

Netherlands (Moens & Vincx, 1998).

Nematodes:

Bacterivorous nematodes used belonged to two species of Monhysteridae,

Diplolaimelloides meyli and D. oschei, and a species of Rhabditidae, Litoditis marina,

which were all harvested from permanent monospecific, agnotobiotic cultures on agar

(Moens & Vincx, 1998). These cultures were maintained under identical temperature

(20°C) and salinity (20‰) conditions. Diplolaimelloides species are opportunistic

colonizers of various types of decaying organic matter (Warwick, 1987; Moens &

5

Vincx 2000). Likewise, Litoditis marina is associated with decomposing macroalgae in

the littoral zone of coastal and estuarine environments (Moens & Vincx 2000; De

Meester et al., 2012). The three nematode species used in this experiment belong to the

same functional group, the deposit feeders, and are assumed to feed mainly or

exclusively on bacteria (Moens & Vincx, 1997). However these species showed

differential responses to food availability; L. marina usually is found in patches

characterized by very high food density where it feeds continuously, while the two

species of Monhysteridae, D. meyli and D. oschei, prefer lower food densities (dos

Santos et al. 2008) and feed more intermittently (Moens et al., 2004). Therefore the

rhabditid species is classified as an enrichment opportunist and both monhysterid

species as general opportunists (dos Santos et al. 2008).

Enoploides longispiculosus and Adonchoaimus fuscus were used as predators,

since the predatory feeding behaviour of both species has been previously well studied

(Moens & Vincx, 1997; Moens et al., 1999; Moens et al., 2000; Hamels et al., 2001;

Gallucci et al., 2005; Moens et al., 2005; dos Santos & Moens, 2011). E.

longispiculosus feeds on nematodes, ciliates and other meio- to microsized benthic

organisms and may be able to even control prey biomass and community structure

(Moens et al., 2000; Hamels et al., 2001; Gallucci et al., 2005; dos Santos & Moens,

2011). A tracer experiment failed to detect uptake of bacterial carbon by

E.longispiculosus (Moens et al., 1999), but recent evidence suggests this species is not a

strict predator and may supplement its diet with microalgae (Moens et al., 2014).

Nevertheless, predation is considered the main feeding strategy for E.longispiculosus.

By contrast, it has been suggested that A.fuscus, like other oncholaimids, is only a

facultative predator which presents additional feeding strategies besides predation

(Moens & Vincx, 1997); scavenging of dead metazoans may be one important feeding

strategy in this species (Moens & Vincx, 1997). On the other hand, in a tracer

experiment, it ingested measurable but very small amounts of bacteria (Moens et al.,

1999).

The predator–prey combinations used in our experiments are artificial, as these

particular predator species are not usually found in the same type of sediments as these

prey species. However, at the Paulina site, they occur at very nearby stations (separated

by only a few tens of meters). This artificial assemblage was chosen for several reasons:

(1) E. longispiculosus and A. fuscus are very common marine predatory nemtodes and

6

survive well in laboratory incubations on different substrata, including agar, for periods

up to several weeks (Moens et al., 1999); (2) D.meyli, D.oschei and L.marina are easy

to culture in the lab, and their life history and, in the case of Diplolaimelloides sp., their

mutual interactions have been well studied (Moens & Vincx, 1998; Moens & Vincx,

2000; De Mesel et al., 2006; dos Santos et al., 2008; dos Santos et al., 2009; dos Santos

& Moens, 2011), moreover (3) they are suitable prey to E. longispiculosus and A. fuscus

(Moens et al., 1999; Gallucci et al., 2005; dos Santos & Moens, 2011), and (4) they

have been shown to measurably affect decomposition rates (De Mesel et al., 2003; De

Mesel et al., 2006) and bacterial assemblage composition (De Mesel et al., 2004; De

Mesel et al., 2006). Therefore despite the fact that the tested assemblage is artificial, we

believe that it is suitable for our aims.

Nematodes were harvested from the agar plates. In the case of Diplolaimelloides

sp. the agar became fully liquid, thus we were able to pour the medium over a sieve;

whereas in the case of L.marina the agar remained solid, hence it was necessary to

harvest them by sucrose washing. L. marina cultures were washed with sucrose in a

final concentration of 40% to remove agar remains and most adhering microbiota,

rinsed 4 times in artificial seawater (ASW), and eventually resuspended in it (Moens &

Vincx, 1998). We counted five aliquots from the resulting suspensions and

consequently we calculated how much suspension we needed to inoculate 500 prey

nematodes to each replicate. In the case of Diplolaimelloides sp., we added 250

individuals of D. oschei and 250 individuals of D. meyli. In both cases the resulting

nematode suspension may be concentrated to the desired density by centrifugation

(1500rpm, 10 min) or diluted by addition of ASW. The ASW used in all our tests had a

salinity of 22 ‰, which is a proper salinity for the nematodes used (Moens & Vincx,

1998; Moens & Vincx 2000).

On the other hand, the predatory nematodes E. longispiculosus and A. fuscus,

were collected from the sandy sediment from the Paulina Polder tidal flat (see Gallucci

et al., 2005, for location of stations with high abundances of these species). Nematodes

were extracted alive from fresh sediment by repeated decantation with tap water over a

125 μm mesh sieve. We collected the retained nematodes on the sieve with ASW, and

handpicked E. longispiculosus and A. fuscus under a compound microscope, specifically

50 individuals per replicate and genus. The collected predator nematodes were rinsed in

ASW to remove adhering organisms, and kept in ASW during one or two days to allow

7

voiding of gut contents. Then this ASW was distributed between all microcosms to be

sure that any bacteria introduced in a predator treatment because of its association with

the predator would also be present in non-predator treatments. Specifically 50µl of

ASW in which E. longispiculosus had been kept, were added to each microcosm

without E. longispiculosus; and 50µl of ASW in which A. fuscus had been kept, were

added to each microcosm without A. fuscus.

Preparation of a bacterial inoculum:

Bacteria were extracted from natural sediments. Sandy intertidal marine

sediment (100ml) from Paulina was rehydrated, mixed and shaken with autoclaved and

filtered (0.2 μm) artificial seawater (salinity of 22). To obtain an uniform bacteria

community in all the treatments from the same experiment, a few drops of agar from the

cultures of corresponding prey nematodes (L. marina or D. meyli and D. oschei) were

added to the resulting slurry, such that when we added these nematodes, the transfer of

bacteria specific for these cultures would not make differences between treatments

without these nematodes and associated bacteria. Then this slurry of sediment and agar

drops was filtered 5 times using 1.2µm Whatman GF/A filters to separate bacteria from

benthic microalgae and heterotrophic protists.

Extracellular Enzymatic Activities:

Two types of exo-enzymatic activity were evaluated as proxies of microbial

activity in microcosms. The exo-enzymatic activity was measured via fluorescence

using the spectrophotometer ‘VICTOR™ Multilabel Plate Reader’. In particular,

activities of ß-D-glucosidase and aminopeptidase were quantified fluorometrically using

fluorogenic analogues, respectively, L-Leucine-4-methylcoumarinyl-7-amide (Leu-

MCA) and 4-methylumbelliferone ß-D-glucopyranoside (MUF-Glu) as substrates

whose hydrolysis provides an estimate of the potential degradation rates of the more

readily available fractions of organic matter in the sediment (Hoppe, 1993; Danovaro et

al., 2001),. The enzymatic analysis was performed on sediment slurries following the

protocol of Danovaro et al., (2001). A sample (0.3g) of sediment was collected

randomly on the sediment column of each microcosm and mixed with 0.2 μm-pore-

8

filtered autoclaved ASW. The enzymatic reactions were started by adding 15μM of the

corresponding enzymatic analogue (Glu-MUF or Leu-MCA) (1mM) in each sediment

sample. A first measurement (blanks) was taken immediately after reading its

fluorescence at 355nm excitation and 460nm emission wavelength. Then incubations

were performed in the dark and at in situ temperature for 1 hour in the experiment with

L. marina and 2 hours in the experiments with Diplolaimelloides sp. The incubation

time in the dark was extended from 1 to 2 hours to allow a longer time for the

enzymatic action on the organic substrates added in the assay. After that the

fluorescence was measured again. Standards (0.1-10µM) to compare with our results

were freshly prepared diluting the stock solution (5mM) in pre-filtered and autoclaved

ASW. Finally the fluorescence of each sample was converted into nmol of MUF or

MCA released per g of sediment dry weight according to the formula:

nmol (MUF or MCA)/g*h=(((Fluoc-Fluoblk)*C+K)*v) / Ps*Tinc

where: MUF or MCA: hydrolyzed fluorogenic analogues of our target enzymes

Fluoc: Fluorescence of the sediment sample

Fluoblk: Fluorescence of the sediment blank

C: slope of the linear regression obtained by calibration curve

K: constant of the linear regression obtained by calibration curve

v: volume of the slurry in ml

Ps: sediment dry weight in grams

Tinc: incubation time in hours

Bacteria pre-tests:

The sediment used for all pre-tests and final experiments was washed with fresh

water and decanted to remove the fauna. The sediment was then dried at 180ºC for 20

hours. Afterwards it was washed again with fresh water and sieved between a 1000µm

sieve and a 32µm sieve to remove the bigger particles and the salts as much as possible.

Ultimately, it was dried again at 180ºC for 20 hours. Because these sediments are poor

in organic matter, even more so after the repeated decantation (which removes the small

silt-clay fraction to which much organic matter adsorbs), organic matter had to be added

as a substratum for bacterial growth. Consequently we tested three different substrates

to find one which would support proper microbial growth as assessed from levels of

enzymatic activity. The tested substrates were domestic sugar, rice and potato dextrose.

These pretests were made in 12-wells and 6-wells plates with dry sediment (5 grams)

and a bacteria suspension, which had been pre-incubated during 48h with the respective

9

substrates before addition to the sediment. The enzymatic activity of these microcosms

was measured after 2, 4, 6 and 8 days. Once we had decided that the rice extract was the

best substrate, additional experiments were performed comparing various rice extract

concentrations and with addition of prey and predator nematodes to the microcosms. In

addition the enzymatic activity of sediments to which no bacteria had been added was

measured as a control. This is largely for two reasons: (1) to test for background

fluorescence which can be caused, for instance, by chlorophyll pigments present in the

sediments, and (2) to test for bacterial contaminations.

Experimental designs:

Microcosms for all experiments were prepared in 6-well plates, using dry

sediment as a substrate and bacteria and nematodes (prey and predator) as living

organisms. Considering the results of the pre-tests we performed three sets of

experiments using different bacterivorous nematodes and/or different rice extract

concentrations to assess trophic cascades from predatory nematodes over bacterivores to

bacterial activity. One experiment was made with L. marina and the other two with a

combination of D. oschei and D. meyli using two different concentrations of rice extract.

Two rice extract concentrations were evaluated to compare the effect of the substrate

concentration on bacterial enzymatic activity. These rice extracts were prepared with

rice previously washed with boiled ASW and bacteria suspension in different

concentrations depending on the experiment: 1grain/ml in the experiment with L.marina

(LM) as well as in one of the experiments with Diplolaimelloides sp. (D1); and

5grains/ml in the other experiment with Diplolaimelloides sp. (D2). The bacteria

suspension was incubated with the rice during 48h at 15°C. Likewise we incubated

ASW with the corresponding concentrations of rice for the controls without bacteria of

the different experiments.

The bacteria in the suspensions tended to aggregate around rice, thus after the

incubation the resulting suspensions were shaken to release the bacteria from the rice

properly. The corresponding suspension was added to the sediment distributed in the 6-

wells plates until the sediment moistened completely and the microcosms were pre-

incubated. The pre-incubation of the bacteria in the sediment allowed build-up of the

bacterial population. Particularly in the experiment with L.marina, 3.5ml of rice extract

10

suspension was added to each replicate with 11g of dry sediment. The microcosms were

pre-incubated for 48h at 15°C. In contrast, in the experiments with Diplolaimelloides sp.

3ml of the corresponding suspension was added to each replicate with 10g of dry

sediment. The extra gram in the experiment with L.marina was kept in the freezer for

further analyses of the microbial community in posterior studies. Because enzymatic

activities in the L. marina experiments tended to be rather low, the pre-incubation time

was increased to 4 days and the incubation temperature was changed to 18ºC to raise the

bacteria activity in the experiments with Diplolaimelloides sp.

After pre-incubation, we did the first measurement (T0) of enzymatic activity of

bacteria for each sediment microcosm (a well in a 6-well plate). Subsequently, we

inoculated the corresponding species and number of nematodes to microcosms. Four

replicates were made for each treatment and control. In both set-ups the controls and

treatments were (Figure 2): rice extract without bacteria (Ar); bacteria suspension (B);

bacteria suspension + bacterial-feeding nematodes (BP); bacteria suspension + E.

longispiculosus (BE); bacteria suspension + A. fuscus (BA); bacteria suspension +

bacterial-feeding nematodes + E. longispiculosus (BPE); bacteria suspension +

bacterial-feeding nematodes + A. fuscus (BPA). Besides we conducted two extra

controls following the protocol for the experiments with Diplolaimelloides species. The

first control was made only with sediment and autoclaved ASW (A) and the second one

was made with fresh sediment (FS) with its natural fauna from Paulina Polder.

Figure 2: Experimental design.

All microcosms with nematodes were incubated for 8 days at 15°C in the case of

the experiment with L. marina and at 18ºC in the case of experiments with

Diplolaimelloides sp. After 2 (T1), 4 (T2), 6 (T3) and 8 (T4) days the enzymatic activity

11

of bacteria was measured. At the end of the experiment all the replicates were decanted

and the nematodes were collected on a sieve with a mesh size of 32µm and counted.

Data Analysis

The data of our experiments did not fit the assumptions for parametric analyses.

Therefore our data was assessed by two-way crossed and nested PERMANOVA

(Permutational Multivariate Analysis of Variance), a non-parametric, permutation-based

statistic for the analysis of multivariate data in response to treatments in an experimental

design. The PRIMER (Plymouth Routines In Multivariate Ecological Research) &

PERMANOVA version 6.1.6, PRIMER-E Ltd. (2006) software was used to evaluate the

variability between treatments of each experiment and the factors promoting this

variability, i.e the main components of variation. Analyses were based on a resemblance

matrix of Euclidean distances and permutations of residuals under a reduced model

(9999 permutations). Pair-wise comparisons were performed (9999 permutations) for

the factors of interest, i.e. treatments and time.

RESULTS:

Experiment with Litoditis marina (LM):

There is statistically significant interaction in the effects of Treatments and Time

on variability in the activity of ß-D-glucosidase (PERMANOVA TrxTi p<0.05; Table

1). This interaction means that the effects of a given factor are different when it is

considered separately within each level of the other factor. The presence of a significant

interaction generally indicates that the effects of each factor alone may not be

meaningful. Therefore our main interest is to examine the interaction effects doing pair-

wise comparisons among levels of the factor Treatment within each level of the factor

Time and vice versa.

12

Table 1: Results from the two –way crossed PERMANOVA analysis of activity ß-D-

glucosidase in response to different treatments and sampling times in the experiment

LM.

Source df SS MS Pseudo-F

P(perm)

Tr 6 5,0241E18 8,3734E17 6,0517 0,0002

Ti 4 2,7624E19 6,9059E18 49,911 0,0001

TrxTi 24 8,1517E18 3,3965E17 2,4548 0,0007

Res 105 1,4528E19 1,3837E17

Total 139 5,5328E19

The pair-wise comparisons testing the ß-D-glucosidase found significant

differences between treatments within each level of the factor Time. At T0, the control

with ASW showed an enzymatic activity significantly lower than the other treatments

(PERMANOVA p<0.05; Figure 3). Unlike what we expected, there were significant

differences between some treatments with predators and without them (PERMANOVA

p<0.05, B, BP > BA, BPE; Figure 3), the former exhibiting lower enzymatic activities

than the latter. At T1, the enzymatic activity in the treatment with E. longispiculosus

was significantly higher than in the treatment with A. fuscus (PERMANOVA p<0.05,

Figure 3). At T2, control ASW only presented significant differences with the treatment

with E. longispiculosus (PERMANOVA p<0.05, Ar < BE; Figure 3), but there were

other significant differences between treatments (PERMANOVA p<0.05, BP, BPA <

BE and B > BPA; Figure 3). At T3, the control ASW showed an enzymatic activity

significantly higher than treatments with nematodes (PERMANOVA p<0.05, Ar > BP,

BA, BPE; Figure 3), and the treatment with only bacteria displayed significant

differences with some treatments with nematodes (PERMANOVA p<0.05, B > BP, BA,

BPE, BPA; Figure 3). Ultimately in the last sampling time (T4) the only significant

differences were between the treatment with bacteria and both treatments with E.

longispiculosus (PERMANOVA p<0.05, B > BE, BPE; Figure 3).

13

Likewise the pair-wise comparisons between sampling times within each level

of the factor Treatment displayed significant differences. Thus the activity of ß-D-

glucosidase varied through time in all the treatments. Particularly, the ß-D-glucosidase

activity displayed a peak of activity at T2, which was reflected in the registered

significant differences in all the treatments (PERMANOVA p<0.05, T0 < T1, T2, T3 in

Ar; T0 < T1, T2, T3, T4 and T2 > T4 in B; T0 < T1, T2, T4 and T1, T2 > T3 in BP; T0,

T4 < T1, T2 and T2 > T1, T3 in BE; T0 < T2, T3 and T2 > T1, T3 in BA; T0 < T1, T2,

T4 and T2 > T3 in BPE; T0 < T2 in BPA; Figure 4). Generally, in all treatments,

G-enzyme L.marina

Mean Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

0

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

9E8

T0

T0: KW-H(6;28) = 18,6133; p = 0,0049

G-enzyme L.marina

Mean Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

2,8E9

T1

T1: KW-H(6;28) = 9,5616; p = 0,1444

G-enzyme L.marina

Mean Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

2,8E9

3E9

T2

T2: KW-H(6;28) = 13,7143; p = 0,0330

G-enzyme L.marina

Mean Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

-2E8

0

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

T3

T3: KW-H(6;28) = 15,7833; p = 0,0150

G-enzyme L.marina

Mean Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

0

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

T4

T4: KW-H(6;28) = 12,7167; p = 0,0478

Figure 3: The mean±SD of activity of

ß-D-glucosidase (nmol/g*h) of 4

replicates by treatment and sampling

time, in the experiment LM. The

capital letters display the significant

differences between treatments.

A B B

A B B C C

A B

A A C

A A B

A B

A A B B B

A B B

14

enzymatic activity increased until T2 and thereafter either remained more or less stable

(Ar, B, BP and BPA) or decreased (BE, BA and BPE).

Box Plot (Spreadsheet en Fini STATISTICA ANOVAS x time 3 exp 24v*20c)

Mean

Mean±SD T0 T1 T2 T3 T4

Var1

0

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

lm-A

SW

r

lm-ASWr: KW-H(4;20) = 10,1; p = 0,0388


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

2,8E9

lm-B

ac

t

lm-Bact: KW-H(4;20) = 13,0429; p = 0,0111


Mean


Var1

-2E8

0

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

lm-B

+L

M

lm-B+LM: KW-H(4;20) = 13,5; p = 0,0091


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

2,8E9

3E9lm

-B+

En

op

loid

es

lm-B+Enoploides: KW-H(4;20) = 14,9286; p = 0,0049


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

2,8E9

lm-B

+A

do

nc

ho

laim

us

lm-B+Adoncholaimus: KW-H(4;20) = 12,2429; p = 0,0156


Mean


Var1

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

lm-B

+L

M+

En

op

lm-B+LM+Enop: KW-H(4;20) = 12,8714; p = 0,0119


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

lm-B

+L

M+

Ad

on

lm-B+LM+Adon: KW-H(4;20) = 10,3571; p = 0,0348






differences between sampling times.

A B B B A B B B

A B C

A B B B

B B C

A B C A/B A

A A/C B C B A

A B B B

A B

15

Similarly for the activity of aminopeptidase, the interaction of the factors

Treatments and Time was also statistically significant (PERMANOVA TrxTi p<0.05;

Table 2).

Table 2: Results from the two –way crossed PERMANOVA analysis of activity

aminopeptidase in response to different treatments and sampling times in the

experiment LM.

Source df SS MS Pseudo-F P(perm)

Tr 6 7,9039E18 1,3173E18 13,451 0,0001

Ti 4 1,0186E19 2,5466E18 26,003 0,0001

TrxTi 24 6,9376E18 2,8907E17 2,9516 0,0002

Res 105 1,0283E19 9,7934E16

Total 139 3,5311E19

The pair-wise comparisons showed significant differences between treatments

within each level of the factor Time. In T0 the only significant differences were

displayed between control ASW and some treatments (PERMANOVA p<0.05, Ar < B,

BP, BPE, BPA; Figure 5). In T1 the treatment with prey presented an enzymatic activity

significantly higher than all the treatments with predator and the control ASW

(PERMANOVA p<0.05, BP > Ar, BE, BA; Figure 5). In T2 the control ASW still

exhibited significantly lower enzymatic activity than some treatments; the treatment

with only bacteria had a significantly higher enzymatic activity than those with only

prey nematodes and with prey nematodes + E. longispiculosus (PERMANOVA p<0.05,

Ar < B, BA, BPE, BPA and B > BP, BPE; Figure 5). In T3 the control ASW and the

treatment with A. fucus presented the lowest enzymatic activity with significant

differences with other treatments (PERMANOVA p<0.05, Ar < B, BP, BPE and BA <

B, BP, BPE, BPA; Figure 5). Finally in T4 there were many significant differences

between treatments (PERMANOVA p<0.05, Ar, BP, BE, BPE < B, BA and BPA > BP,

BE, BA, BPE; Figure 5).

16

The pair-wise comparisons between sampling times within each level of the

factor Treatment also were made. Unlike ß-D-glucosidase activity, there were no

significant differences in the aminopeptidase activity in the control with ASW through

time. Nevertheless, all treatments with nematodes exhibited a decreasing tendency of

the enzymatic activity at T3 and T4, which was reflected in the significant differences in

all the treatments (PERMANOVA p<0.05, T4 < T0, T1, T2, T3 in BP; T4 < T0, T1, T2

in BE; T2 > T1, T3, T4 in BA; T0 > T1, T3, T4 and T4 < T2, T3 in BPE; T2 > T1, T3,

T4 in BPA; Figure 6). Conversely, in the treatment with only bacteria, T1 showed the

lowest enzymatic activity differing significantly from other sampling times

(PERMANOVA p<0.05, T1< T0, T2 in B; Figure 6).

Box Plot (STATISTICA-L.marina 6v*28c)

Mean Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

2,8E9

T0

T0: KW-H(6;28) = 12,4729; p = 0,0522


Mean

Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

T1

T1: KW-H(6;28) = 14,4754; p = 0,0248


Mean Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

T2

T2: KW-H(6;28) = 16,33; p = 0,0121


Mean Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

T3

T3: KW-H(6;28) = 18,1404; p = 0,0059


Mean Mean±SD

AS

Wr

Bact

B+

LM

B+

Enoplo

ides

B+

Adonchola

imus

B+

LM

+E

nop

B+

LM

+A

don

Var1

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

T4

T4: KW-H(6;28) = 23,2611; p = 0,0007


aminopeptidase (nmol/g*h) of 4 replicates

by treatment and sampling time, in the

experiment LM. The capital letters display

the significant differences between

treatments.

A B B B B A B A A

A B B/C C B/C

B D

A B A A C A

A A C A D

A B B A B

A C

17


Mean


Var1

4E8

5E8

6E8

7E8

8E8

9E8

1E9

1,1E9

lm-A

SW

r

lm-ASWr: KW-H(4;20) = 1,0143; p = 0,9076


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

lm-B

act

lm-Bact: KW-H(4;20) = 9,1857; p = 0,0566


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

lm-B

+LM

lm-B+LM: KW-H(4;20) = 11,5; p = 0,0215


Mean


Var1

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

lm-B

+E

no

plo

ide

s

lm-B+Enoploides: KW-H(4;20) = 13,0286; p = 0,0111


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

2,6E9

2,8E9

lm-B

+A

donchola

imus

lm-B+Adoncholaimus: KW-H(4;20) = 9,0286; p = 0,0604


Mean


Var1

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

2,4E9

lm-B

+L

M+

En

op

lm-B+LM+Enop: KW-H(4;20) = 15,5714; p = 0,0037

Mean


Var1

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1,8E9

2E9

2,2E9

lm-B

+L

M+

Ad

on

lm-B+LM+Adon: KW-H(4;20) = 11,5571; p = 0,0210


aminopeptidase (nmol/g*h) of 4





A B A

A A A A B A A A B

A B A A

A B

A A/B B C

A B A A

18

Experiment with Diplolaimelloides sp. (1grain/ml) (D1):

The interaction between the effects of Treatments and Time on variability in the

activity of ß-D-glucosidase was statistically significant (PERMANOVA TrxTi p<0.05;

Table 3).



D1.


Tr 8 2,8895E18 3,6118E17 125,67 0,0001

Ti 4 8,2537E17 2,0634E17 71,792 0,0001

TrxTi 32 1,8416E18 5,755E16 20,023 0,0001

Res 135 3,8801E17 2,8742E15

Total 179 5,9444E18

The pair-wise comparisons made between treatments within each level of the

factor Time displayed significant differences in every sampling time. Particularly, in T0

the controls with ASW and fresh sediment presented, respectively, significant

differences with almost all treatments (PERMANOVA p<0.05, FS > A, Ar, B, BP, BE,

BA, BPE, BPA; A < Ar, B, BP, BE, BA, BPE, BPA and Ar > BE, BPA; Figure 7). In

the T1 also the controls differed significantly from the rest of the treatments, besides

there were significant differences between these treatments (PERMANOVA p<0.05, FS

> A, Ar, B, BP, BE, BA, BPE, BPA; A < Ar, B, BP, BE, BA, BPE; Ar < B, BP, BE,

BA, BPE; BP < BE and BPA < B, BP, BE, BA; Figure 7). In T2 the significant

differences were between controls and treatments as well as between treatments with

predator and treatment with prey (PERMANOVA p<0.05, FS > A, BP, BPE, BPA; FS

< B, BE, BA; A < Ar, B, BP, BE, BA, BPA; Ar > BP, BPE, BPA; Ar < BA and B, BE,

BA > BP, BPE, BPA; Figure 7). Likewise in the T3 the controls differed significantly

from the rest of the treatments as well as these treatments displayed significant

differences (PERMANOVA p<0.05, FS >A, BP, BPE, BPA; FS < B, BA; A < B, BP,

BE, BA and B, BE, BA > BP; Figure 7). In the T4 the enzymatic activity in the control

FS was significantly higher than the rest of treatments and only some treatments

presented significant differences between them (PERMANOVA p<0.05, FS > A, Ar, B,

BP, BE, BA, BPE, BPA; BA > BP, BPE, BPA and Ar > BPE; Figure 7).

19

G-enzyme Diplo 1

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E8

0

1E8

2E8

3E8

4E8

5E8

6E8

T0

T0: KW-H(8;36) = 28,1712; p = 0,0004

G-enzyme Diplo 1

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E8

0

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

9E8

T1

T1: KW-H(8;36) = 31,1937; p = 0,0001

G-enzyme Diplo 1

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E8

0

1E8

2E8

3E8

4E8

5E8

6E8

7E8

T2

T2: KW-H(8;36) = 31,4279; p = 0,0001

G-enzyme Diplo 1

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E8

0

1E8

2E8

3E8

4E8

5E8

T3

T3: KW-H(8;36) = 29,2613; p = 0,0003

G-enzyme Diplo 1

Mean

Mean±SD

Fre

sh

se

d

AS

W

AS

W r

Ba

ct

B+

Dip

lo

B+

En

op

loid

es

B+

Ad

on

ch

ola

imu

s

B+

Dip

lo+

En

op

B+

Dip

lo+

Ad

on

Var1

-1E8

0

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

T4

T4: KW-H(8;36) = 23,1532; p = 0,0032

Figure 7: The mean±SD of activity

of ß-D-glucosidase (nmol/g*h) of 4

replicates by treatment and

sampling time, in the experiment

D1. The capital letters display the

significant differences between

treatments.

C D D

A B C C C C C C C

A D D D D F

D E

A B C D D D D D

B C B B

C B

A B B B B B B B B

A B C D A/C C B B

A C

A B A/C C D C C B/D D

20

The obtained results for the ß-D-glucosidase activity showed the same tendency

through time in all treatments as well as in the control Ar. The enzymatic activity

started low, then increased and finally decreased. The significant differences found

between treatments supported this hypothesis (PERMANOVA p<0.05, T2 > T0, T1, T3,

T4 and T1 < T4 in Ar; T2 > T3 > T1 > T0, T4 in B; T0, T4 < T1, T2, T3 in BP; T0 <

T1, T2, T3, T4; T4 < T1, T2, T3 and T1 < T2 in BE; T2 > T3 > T1 > T4 > T0 in BA;

T0, T4 < T1 in BPE; T2 > T0, T3, T4 in BPA; Figure 8). However, the control A

displayed an increasing ß-D-glucosidase activity through time (PERMANOVA p<0.05,

T0 < T3, T4; T1 < T0, T2, T3, T4 and T2 < T4; Figure 8). On the other hand, the

control with fresh sediment showed no a clear tendency and there were significant

differences between sampling times (PERMANOVA p<0.05, T3 < T0, T2 < T1, T4;

Figure 8).

21


T0 T1 T2 T3 T4

Var1

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

9E8

Fre

sh s

ed

Fresh sed: KW-H(4;20) = 16,5286; p = 0,0024


T0 T1 T2 T3 T4

Var1

-6E7

-4E7

-2E7

0

2E7

4E7

6E7

8E7

1E8

1,2E8

1,4E8

1,6E8

1,8E8

AS

W

ASW: KW-H(4;20) = 15,2; p = 0,0043


T0 T1 T2 T3 T4

Var1

5E7

1E8

1,5E8

2E8

2,5E8

3E8

3,5E8

4E8

4,5E8

5E8

1-A

SW

r

1-ASW r: KW-H(4;20) = 12,7286; p = 0,0127


T0 T1 T2 T3 T4

Var1

0

1E8

2E8

3E8

4E8

5E8

6E8

1-B

ac

t

1-Bact: KW-H(4;20) = 17,8857; p = 0,0013


T0 T1 T2 T3 T4

Var1

4E7

6E7

8E7

1E8

1,2E8

1,4E8

1,6E8

1,8E8

2E8

2,2E8

2,4E8

1-B

+D

iplo

1-B+Diplo: KW-H(4;20) = 14,2571; p = 0,0065


T0 T1 T2 T3 T4

Var1

0

1E8

2E8

3E8

4E8

5E8

6E8

7E8

1-B

+E

no

plo

ide

s

1-B+Enoploides: KW-H(4;20) = 17,0857; p = 0,0019


T0 T1 T2 T3 T4

Var1

0

1E8

2E8

3E8

4E8

5E8

6E8

1-B

+A

donchola

imus

1-B+Adoncholaimus: KW-H(4;20) = 18,2857; p = 0,0011


T0 T1 T2 T3 T4

Var1

-2E7

0

2E7

4E7

6E7

8E7

1E8

1,2E8

1,4E8

1,6E8

1,8E8

2E8

2,2E8

2,4E8

2,6E8

1-B

+D

iplo

+E

no

p

1-B+Diplo+Enop: KW-H(4;20) = 8,7429; p = 0,0679


T0 T1 T2 T3 T4

Var1

-4E7

-2E7

0

2E7

4E7

6E7

8E7

1E8

1,2E8

1,4E8

1,6E8

1,8E8

2E8

2,2E8

1-B

+D

iplo

+A

don

1-B+Diplo+Adon: KW-H(4;20) = 10,3571; p = 0,0348

Figure 8: The mean±SD of

activity of ß-D-glucosidase

(nmol/g*h) of 4 replicates by

treatment and sampling time, in

the experiment D1. The capital

letters display the significant

differences between sampling

times.

A B A C B

A B C C

B A C

C D

A A B A A

A B C D A

A B B B A

A B C B/C D

A B C D E

A B A

A B A A

22

We also found statistically significant interaction in the effects of Treatments

and Time on variability in the activity of aminopeptidase (PERMANOVA TrxTi

p<0.05; Table 4).



experiment D1.


Tr 8 2,6167E20 3,2709E19 111

0,0001

Ti 4 1,4925E20 3,7312E19 126,62

0,0001

TrxTi 32 1,132E20 3,5376E18

12,005 0,0001

Res 135 3,9782E19 2,9468E17

Total 79 5,639E20

As in the measurements of ß-D-glucosidase activity, the pair-wise comparisons

between treatments within each level of the factor Time concerning to aminopeptidase

activity displayed significant differences between the controls and the treatments with

bacteria in all the sampling times (PERMANOVA p<0.05, FS > B, BP, BE, BA, BPE,

BPA > Ar > A in T0; FS, B, BP, BE, BA, BPE, BPA > Ar > A and FS > BP, BE, BPE,

BPA in T1; B, BP, BE, BA, BPE, BPA > FS > Ar > A in T2; FS, B, BP, BE, BA, BPE,

BPA > Ar > A and FS < B. BP, BA, BPE in T3; FS > B, BP, BE, BA, BPE, BPA > Ar

and A < FS, Ar, BP, BPE in T4; Figure 9). Moreover in T1, T3, T4 there were

significant differences between treatments (PERMANOVA p<0.05, BPE > BPA in T1;

B >BE in T3; BE < B, BP, BA, BPE in T4; Figure 9).

23

Besides the aminopeptidase activity in this experiment exhibited the same

tendency through time as for ß-D-glucosidase, with an initial increase followed by a

decrease in activity after T2. All significant differences supported this tendency

(PERMANOVA p<0.05, T2, T3 > T0, T1 and T1 < T4 in Ar; T2, T3 > T1, T4 > T0 in

B, BP, BE, BA, BPE, BPA; Figure 10). However the control A presented no significant

differences, i.e. the activity did not change through time. On the other hand, the control

FS fluctuated a lot, without clear tendency, showing significant differences between all

sampling times (PERMANOVA p<0.05, T0, T1, T2, T3 < T4 and T2 < T1, T3; Figure

10).

P-enzyme Diplo 1

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-5E8

0

5E8

1E9

1,5E9

2E9

2,5E9

3E9

3,5E9

T0

T0: KW-H(8;36) = 28,5225; p = 0,0004

P-enzyme Diplo 1

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-5E8

0

5E8

1E9

1,5E9

2E9

2,5E9

3E9

3,5E9

4E9

T1

T1: KW-H(8;36) = 27,3288; p = 0,0006 P-enzyme Diplo 1

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E9

0

1E9

2E9

3E9

4E9

5E9

6E9

7E9

8E9

T2

T2: KW-H(8;36) = 27,6216; p = 0,0006

P-enzyme Diplo 1

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E9

0

1E9

2E9

3E9

4E9

5E9

6E9

T3


Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-2E9

-1E9

0

1E9

2E9

3E9

4E9

5E9

6E9

7E9

T4

T4: KW-H(8;36) = 27,982; p = 0,0005


aminopeptidase (nmol/g*h) of 4


time, in the experiment D1. The capital

letters display the significant differences

between treatments.

A B C D D D D D D

A B C D D D D D D

D E E E E

E A

A B C A A A A A A

D A

A B C D D D/A D D D/A

A B C C D C C C/D

B C C

24

T0 T1 T2 T3 T4

Var1

1E9

2E9

3E9

4E9

5E9

6E9

7E9

Fre

sh s

ed

Fresh sed: KW-H(4;20) = 16,1857; p = 0,0028


T0 T1 T2 T3 T4

Var1

-1,5E9

-1E9

-5E8

0

5E8

1E9

1,5E9

2E9

2,5E9

AS

W

ASW: KW-H(4;20) = 1,0286; p = 0,9054


T0 T1 T2 T3 T4

Var1

2E8

4E8

6E8

8E8

1E9

1,2E9

1,4E9

1,6E9

1-A

SW

r

1-ASW r: KW-H(4;20) = 15,8714; p = 0,0032


T0 T1 T2 T3 T4

Var1

1E9

2E9

3E9

4E9

5E9

6E9

1-B

act

1-Bact: KW-H(4;20) = 16,8143; p = 0,0021 Box Plot (Spreadsheet en Fini STATISTICA ANOVAS x time 3 exp 24v*20c)

T0 T1 T2 T3 T4

Var1

1E9

1,5E9

2E9

2,5E9

3E9

3,5E9

4E9

4,5E9

5E9

5,5E9

1-B

+D

iplo

1-B+Diplo: KW-H(4;20) = 16,8286; p = 0,0021


T0 T1 T2 T3 T4

Var1

0

1E9

2E9

3E9

4E9

5E9

6E9

7E91-B

+E

noplo

ides

1-B+Enoploides: KW-H(4;20) = 17,0286; p = 0,0019 Box Plot (Spreadsheet en Fini STATISTICA ANOVAS x time 3 exp 24v*20c)

T0 T1 T2 T3 T4

Var1

0

1E9

2E9

3E9

4E9

5E9

6E9

7E9

8E9

1-B

+A

donchola

imus



T0 T1 T2 T3 T4

Var1

1E9

2E9

3E9

4E9

5E9

6E9

7E9

1-B

+D

iplo

+E

nop

1-B+Diplo+Enop: KW-H(4;20) = 17,0286; p = 0,0019


T0 T1 T2 T3 T4

Var1

1E9

1,5E9

2E9

2,5E9

3E9

3,5E9

4E9

4,5E9

5E9

5,5E9

6E9

1-B

+D

iplo

+A

don

1-B+Diplo+Adon: KW-H(4;20) = 16,8143; p = 0,0021


activity of aminopeptidase






times.

C D C

A A A A B

C D

A A B B

A B C C B

A B C C B

A B C C B

A B C C B

A B C C B

A B C

A B C B

25

Experiment with Diplolaimelloides sp. (5grains/ml) (D2):

The interaction between the effects of Treatments and Time on variability in the

activity of ß-D-glucosidase was statistically significant (PERMANOVA TrxTi p<0.05;

Table 5).



D2.


Tr 8 3,0696E18 3,837E17 40,92 0,0001

Ti 4 2,4667E18 6,1668E17 65,765 0,0001

TrxTi 32 2,3221E18 7,2566E16 7,7387 0,0001

Res 135 1,2659E18 9,377E15

Total 179 9,1244E18

The ß-D-glucosidase activity showed a similar behavior as in the previous

experiment D1. The control A presented the lowest enzymatic activity at all sampling

times, differing significantly from the rest of treatments, except in the T4

(PERMANOVA p<0.05, A< FS, Ar, B, BP, BE, BA, BPE, BPA in T0, T1, T3; A< FS,

Ar, B, BP, BE, BA, BPE in T2; A< FS in T4; Figure 11). In contrast, the enzymatic

activity in the controls Ar and FS fluctuted through time; there were few significant

differences between treatments at every sampling time (PERMANOVA p<0.05, FS >

Ar, B, BP, BE, BA, BPE, BPA; B, BPA < BA and BE > BPA in T0; Ar < FS, B, BP,

BE, BA, BPE, BPA; FS > BP and BE, BA > BPE, BPA in T1; FS, Ar < BE; B,BE, BA

> BPA and BP < B, BE in T2; FS > BPE; Ar > FS, BP, BA, BPE, BPA and BP, BPE <

BA in T3; Ar > BP in T4; Figure 11).

26

The obtained results for the ß-D-glucosidase activity showed the same tendency

through time than in the experiment D1. The enzymatic activity started low, then

increased and finally decreased, which was reflected in the registered significant

differences in all the treatments (PERMANOVA p<0.05, T0, T1, T4 < T2, T3 in Ar; T0,

T4 < T1, T2 and T2 >T3 in B; T0, T2, T3, T4 < T1 in BP; T0, T3, T4 < T1, T2 in BE;

T4 < T0, T3 < T2 < T1 in BA; T0, T3, T4 < T1, T2 and T0> T3 in BPE; T0, T3, T4 <

T1 in BPA; Figure 12). The control A and FS were shared between the experiment D1

G-enzyme Diplo 2

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E8

0

1E8

2E8

3E8

4E8

5E8

6E8

T0

T0: KW-H(8;36) = 26,4414; p = 0,0009

G-enzyme Diplo 2

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-2E8

0

2E8

4E8

6E8

8E8

1E9

T1

T1: KW-H(8;36) = 27,0856; p = 0,0007

G-enzyme Diplo 2

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-2E8

0

2E8

4E8

6E8

8E8

1E9

T2

T2: KW-H(8;36) = 25,5495; p = 0,0013

G-enzyme Diplo 2

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-5E7

0

5E7

1E8

1,5E8

2E8

2,5E8

3E8

3,5E8

4E8

4,5E8

T3

T3: KW-H(8;36) = 22,8604; p = 0,0035 G-enzyme Diplo 2

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E8

0

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

T4

T4: KW-H(8;36) = 21,3739; p = 0,0062




time, in the experiment D2. The capital



A B C C C C C C C

C D C

E C

A B C A A A A A A

D A A D D

A B A A D

B A A A

A C A A A A C

B C

A B B B B B B B B

A A/E D E

A B C A/C A A/C A A A

27

and D2, therefore the same significant differences between them were observed in both

experiments.


Mean


Var1

1,2E8

1,4E8

1,6E8

1,8E8

2E8

2,2E8

2,4E8

2,6E8

2,8E8

3E8

3,2E8

3,4E8

3,6E8

3,8E8

4E8

4,2E8

4,4E82

-AS

W r

2-ASW r: KW-H(4;20) = 14,5857; p = 0,0056


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

9E8

1E9

2-B

ac

t


Mean


Var1

5E7

1E8

1,5E8

2E8

2,5E8

3E8

3,5E8

4E8

4,5E8

5E8

2-B

+D

iplo

2-B+Diplo: KW-H(4;20) = 11,2143; p = 0,0243


Mean


Var1

0

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

9E8

1E9

2-B

+E

no

plo

ide

s


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

9E8

2-B

+A

donchola

imus



Mean Mean±SD

T0 T1 T2 T3 T4

Var1

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

2-B

+D

iplo

+E

no

p

2-B+Diplo+Enop: KW-H(4;20) = 16,8286; p = 0,0021 Box Plot (Spreadsheet en Fini STATISTICA ANOVAS x time 3 exp 24v*20c)

Mean Mean±SD

T0 T1 T2 T3 T4

Var1

-1E8

0

1E8

2E8

3E8

4E8

5E8

6E8

7E8

8E8

2-B

+D

iplo

+A

don

2-B+Diplo+Adon: KW-H(4;20) = 10,3; p = 0,0357


activity of ß-D-glucosidase






times.

A A B B A

B A

A B B A

A B A A A

A B B A A

A B C A D

A B B C A/C

A B A A

28

Regarding to the activity of aminopeptidase we also found statistically

significant interaction in the effects of Treatments and Time (PERMANOVA TrxTi

p<0.05; Table 6).



experiment D2.


Tr 8 8,0719E20 1,009E20 197,92 0,0001

Ti 4 1,4036E20 3,5091E19 68,834 0,0001

TrxTi 32 1,0633E20 3,3228E18 6,518 0,0001

Res 135 6,8822E19 5,0979E17

Total 179 1,1227E21

The aminopeptidase activity also displayed a similar behaviour as in the

experiment D1. The control ASW showed the lowest enzymatic activity, followed by

the control ASWr. Both controls exhibited significant differences compared to the other

treatments (PERMANOVA p<0.05, A < Ar < FS, B, BP, BE, BA, BPE, BPA in T0, T1,

T2, T4; A < Ar, FS < B, BP, BE, BA, BPE, BPA in T3; Figure 13). Significant

differences were also found between treatments and control FS, but only few

differences between treatments with bacteria were statistically significant

(PERMANOVA p<0.05, FS < B < BPA; FS < BPE and FS, B, BP, BPE < BA in T0;

FS, B, BP < BA, BPA and FS < B, BE, BPE in T1; FS < B, BP < BA, BPE, BPA and

FS, B < BE in T2; B, BP < BA, BPE, BPA in T3; FS, B, BP, BA < BPE and B < BA,

BPA in T4; Figure 13).

29

The same tendency was observed in the aminopeptidase activity as in the

experiment D1 for all the treatments. All significant differences supported this tendency

(PERMANOVA p<0.05, T0 < T2,T3, T4 in Ar; T0 < T1, T3, T4 < T2 in B; T0, T1 <

T2; T2 > T3 and T0 < T3, T4 in BP; T0 < T1, T2, T3, T4 in BE; T0 < T4 < T1, T2, T3

in BA; T0 < T1 < T2, T3, T4 in BPE; T0 < T1 < T2, T3 and T0 < T4 < T2 in BPA;

Figure 14). The controls A and FS were shared between the experiment D1 and D2,

therefore the same significant differences were observed in both experiments.

P-enzyme Diplo 2

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E9

0

1E9

2E9

3E9

4E9

5E9

6E9

T0

T0: KW-H(8;36) = 29,6171; p = 0,0002

P-enzyme Diplo 2

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E9

0

1E9

2E9

3E9

4E9

5E9

6E9

7E9

8E9

9E9

T1


Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-2E9

0

2E9

4E9

6E9

8E9

1E10

1,2E10

T2

T2: KW-H(8;36) = 30,8919; p = 0,0001

P-enzyme Diplo 2

Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-1E9

0

1E9

2E9

3E9

4E9

5E9

6E9

7E9

8E9

9E9

T3


Mean Mean±SD

Fre

sh s

ed

AS

W

AS

W r

Bact

B+

Dip

lo

B+

Enoplo

ides

B+

Adonchola

imus

B+

Dip

lo+

Enop

B+

Dip

lo+

Adon

Var1

-2E9

0

2E9

4E9

6E9

8E9

1E10

T4

T4: KW-H(8;36) = 27,5135; p = 0,0006


of aminopeptidase (nmol/g*h) of 4


time, in the experiment D2. The



A B A C C C/D D D D F F F F E

A B C D D E E E

F F F F F E E

A B C D A/D D D

A G

F F F F E

A B C A A A/D D A

A D D

D D D D D D E

A B C A A A A A

30


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

1,8E9

2E9

2,2E9

2,4E9

2,6E9

2,8E9

3E9

3,2E9

3,4E9

2-A

SW

r

2-ASW r: KW-H(4;20) = 13,1; p = 0,0108


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

2,5E9

3E9

3,5E9

4E9

4,5E9

5E9

5,5E9

6E9

6,5E9

7E9

2-B

ac

t


Mean Mean±SD

T0 T1 T2 T3 T4

Var1

3E9

3,5E9

4E9

4,5E9

5E9

5,5E9

6E9

6,5E9

7E9

7,5E9

2-B

+D

iplo

2-B+Diplo: KW-H(4;20) = 16,1714; p = 0,0028


Mean


Var1

2E9

3E9

4E9

5E9

6E9

7E9

8E9

9E9

1E10

1,1E10

2-B

+E

no

plo

ide

s


Mean


Var1

3E9

4E9

5E9

6E9

7E9

8E9

9E9

1E10

2-B

+A

do

nc

ho

laim

us



Mean Mean±SD

T0 T1 T2 T3 T4

Var1

3E9

4E9

5E9

6E9

7E9

8E9

9E9

1E10

2-B

+D

iplo

+E

no

p

2-B+Diplo+Enop: KW-H(4;20) = 15,3286; p = 0,0041 Box Plot (Spreadsheet en Fini STATISTICA ANOVAS x time 3 exp 24v*20c)

Mean Mean±SD

T0 T1 T2 T3 T4

Var1

3E9

4E9

5E9

6E9

7E9

8E9

9E9

2-B

+D

iplo

+A

don

2-B+Diplo+Adon: KW-H(4;20) = 15,6143; p = 0,0036


of aminopeptidase (nmol/g*h) of 4


time, in the experiment D2. The



A B B B A B C B/C B

A B C B/C

A B A B B B B

A B B C

A B

A C B

A B C C

A B C C C

31

Nested analysis of experiments D1 and D2:

On the other hand, the results of the experiments D1 and D2 were examined with

a nested PERMANOVA design to better evaluate the effect of the different rice extracts

on the variability of the enzymatic activity. The Treatment factor was nested within

Rice extract level and in turn the Time factor was nested within Treatment (Figure 15).

The amount of variation that was attributable to the different levels in the model was

estimated by the components of variation for these levels (Table 7).

Figure 15: Schematic diagram of the sampling design for enzymatic activity.

Rice extracts 1 ml /grain

Treatments: Ar B BP BE BA BPE BPA

Time: T0 T1 T2 T3 T4

Etc.

n = 4 replicates

Rice extracts 0.2 ml /grain

Treatments: Ar B BP BE BA BPE BPA

Time: T0 T1 T2 T3 T4

Etc.

n = 4 replicates

32

Table 7: Results of nested PERMANOVA analysis of activity ß-D-glucosidase in

response to different rice extracts, treatments and sampling times in the experiments D1

and D2.

Source df SS MS Pseudo-F

P(perm)

Ri 1 1,4028E18 1,4028E18 11,195

0,0084

Tr(Ri) 12 1,5037E18 1,2531E17 1,1607

0,3259

Ti(Tr(Ri)) 56 6,0454E18 1,0795E17 15,671

0,0001

Res 210 1,4466E18 6,8886E15

Total 279 1,0398E19

Estimates of components of variation

Source Estimate Sq.root

S(Ri) 9,1251E15 9,5526E7

V(Tr(Ri)) 8,6766E14 2,9456E7

V(Ti(Tr(Ri))) 2,5266E16 1,5895E8

V(Res) 6,8886E15 8,2997E7

The obtained results for activity of ß-D-glucosidase indicated that the variability

derived from the Rice extract level and Time level significantly affected the enzymatic

activity (PERMANOVA p<0.05, Table 7). Particularly, the greatest component of

variation occurred at the Time scale, followed by Rice extract (Table 7). However the

Treatment level was the less important term in the model to explain overall variation

(Table 7).

33

Table 8: Results of nested PERMANOVA analysis of activity aminopeptidase in

response to different rice extracts, treatments and sampling times in the experiments D1

and D2.


Ri 1 4,7575E20 4,7575E20 15,777

0,0045

Tr(Ri) 12 3,6185E20 3,0154E19 3,7208

0,0005

Ti(Tr(Ri)) 56 4,5384E20 8,1043E18 19,296

0,0001

Res 210 8,8202E19 4,2001E17

Total 279 1,3796E21

Estimates of components of variation

Source Estimate Sq.root

S(Ri) 3,1828E18 1,7841E9

V(Tr(Ri)) 1,1025E18 1,05E9

V(Ti(Tr(Ri))) 1,9211E18 1,386E9

V(Res) 4,2001E17 6,4808E8

The activity of aminopeptidase showed that all levels in the design contributed

significantly to the variability of the enzymatic activity (PERMANOVA p<0.05, Table

8). The estimates of components of variation indicated that the highest variability

occurred at Rice extract level, followed by Time level and lastly the Treatment scale

(Table 8).

34

DISCUSSION:

We performed experiments to assess effects of nematodes representing two

different trophic levels on microbial activity. Based on previous experiments, we

expected that bacterivorous nematodes would measurably affect microbial activity, and

that predatory nematodes would impact prey nematode abundances. The latter aspect

could not be completed within the time frame of this study, but several earlier

microcosm studies with the predator-prey combinations used in our experiments with

Diplolaimelloides species (Moens et al., 1999; Moens et al., 2000; Gallucci et al., 2005)

allow us to confidently assume that at least in the treatments with E. longispiculosus,

monhysterid prey abundance should have been substantially affected. Hence, we

hypothesized that the presence of predators would at least partly counteract the expected

effect of bacterivorous nematodes on bacterial activity.

We measured the activity of two exo-enzymes as proxies of bacterial activity to

test these cascading effects. The quantitative estimates of bacterial extracellular enzyme

activity may be used as indications of the bacterial growth and the bacterial substrate

uptake, despite these measurements do not provide a complete picture of microbial

activity (Hoppe, 1993). The main advantages of this method compared to more

informative tests, such as the measurement of bacterial carbon production, are a

relatively easy protocol and short incubation periods (Hoppe, 1993). Moreover enzyme

activity in natural sediments might provide valuable information regarding the organic

matter flux in the microbial loop as well as of the quantity and quality of the available

organic matter to heterotrophs (Fabiano & Danovaro, 1998).

In our study both ß-D-glucosidase and aminopeptidase activity showed a similar

temporal pattern in all experiments, except for the aminopeptidase activity in the LM

experiment that exhibited a decreasing tendency of the enzymatic activity after 6-day. In

the rest of experiments, in all the treatments with bacteria a maximum enzymatic

activity was displayed in the middle of the experiments and the enzymatic activity

decreased significantly towards the end of the 8-day incubations. Temporal patterns

were not mainly driven by nematodes, as the patterns appeared in the treatments with

nematodes as well as in the treatments without them. In fact, the estimates of the

components of variation in the nested design for the experiments D1 and D2 supported

that the most (for ß-D-glucosidase) or second most (for aminopeptidase) important

35

source of variability of enzymatic activity was time. We suggest that the decline of

enzymatic activity towards the end of the experiment was due to the depletion of

available nutrients through time, as we started with a poor medium, despite the

provision of external organic matter. The lack of nutrients, as well as the surface area,

has been previously recorded as limiting factors for the growth of microbial populations

in aquatic sediments (Novitsky, 1987). Traunspurger et al. (1997) studied the effects of

bacterivorous nematodes on bacterial activity and abundance in freshwater sediment

running lab experiments during 5, 7 and 17 days. The authors observed that the

presence of nematodes stimulated the activity and abundance of bacteria, but with a

decreasing influence through time, i.e. the effect was strongest in the early phase of the

experiments. Therefore the Time factor might partly mask the direct and/or indirect

effects of the bacterivorous and predatory nematodes on the bacterial activity.

Nevertheless, the factor nematode treatment was also highly significant for

aminopeptidase activity, and in the two-way analysis of all experiments, Treatment

factor always exhibited significant effects. Often, these effects revealed differences

between treatments with nematodes and controls without nematodes. We therefore have

substantial evidence that nematodes in our experiments affected bacterial activity to

some degree. Such effects can be the result of direct or indirect processes.

The response of microbial populations to grazing (direct process) has varied

greatly in previous studies. Bacterivorous nematodes may increase or decrease the

bacterial growth or the abundance of bacteria may remain unaffected by their nematode

grazers (Ingham et al., 1985). In our study the measurements of ß-D-glucosidase

activity showed in all the experiments a similar pattern. The ß-D-glucosidase activity

was significantly higher in the treatment with only bacteria than in the treatments with

bacterial-feeding nematodes, either was not significantly different. Particularly, in the

LM experiment, the ß-D-glucosidase activity in the treatment B was higher than in the

treatment BPA in T2 and in the treatment BPE inT4, as well as the enzymatic activity in

T3 was significantly higher in treatment B than in the treatments BP, BPE and BPA. In

the experiment D1 in T1 the treatment B showed significantly higher activity than

treatment BPA, and in T2 and T3 the treatment B presented significantly higher

enzymatic activity than the treatments BP, BPE and BPA. Ultimately in the experiment

D2 the ß-D-glucosidase activity in the treatment B was significantly higher than in

treatment BP and BPA in T2. This pattern may be due to the direct consumption of

36

bacteria by nematodes, what can sometimes reduce bacteria abundance and activity (De

Mesel et al., 2003; De Mesel et al., 2004; De Mesel et al., 2006; Hubas et al., 2010).

However the measurements of aminopeptidase activity in the diverse

experiments showed no consistent differences between treatments. In the LM

experiment, significant differences were found after 4 and 8 days, with higher

enzymatic activity in the treatment with only bacteria than in the treatments BP and

BPE. In the experiment D1, there were no significant differences in aminopeptidase

activity between treatments with and without nematodes, while in the experiment D2

the treatment B had a lower aminopeptidase activity than the treatments BPE and BPA

at all sampling times. These last results in experiment D2 supported the presence of

stimulatory effects on sediment-living bacteria that have been widely reported not only

in presence of nematodes, but also of other benthic invertebrates (e.g. Fabiano &

Danovaro, 1998; Moriarty 1986; van de Bund et al. 1994). There are different possible

mechanisms for stimulation of bacterial population growth by bacterivorous nematodes,

as described in the introduction. However, our study does not allow elucidating the

mechanisms underlying the observed results. More importantly, nevertheless, it is that

stimulatory effects on microbial activity were exception rather than rule in our

experiments, as the microbial activity was in most cases amongst the highest in the

absence of nematodes.

Moreover, Ingham et al. (1985) suggested that the response of bacterial biomass

to nematode grazing differs with nematode species identity and density. The three

nematode species used in our experiment belong to the same functional group, the

deposit feeders (Moens & Vincx, 1998), however these species showed differential

responses to food availability. Rhabditid nematodes typically graze at higher rates than

monhysterids if sufficiently high bacterial densities are present. Therefore we might

expect a stronger effect of L. marina than of Diplolaimelloides species. Nevertheless the

results in the different experiments did not differ widely. Two possible reasons, non-

mutually exclusive, may explain this situation: 1) bacterial densities were not

sufficiently high for L. marina (dos Santos et al. 2009), and/or 2) bacteria may have be

less available to L. marina in sandy sediments, as their usual habitat is macroalgal

wrack (dos Santos et al. 2009).

37

Specifically, the effects of D. meyli and D. oschei, and other monhysterid

nematodes, on bacterial activity and detritus decomposition rates have been previously

documented (De Mesel et al., 2003; De Mesel et al., 2004; De Mesel et al., 2006;

Hubas et al., 2010). As in our experiments, significant declines of the microbial activity

were recorded even at relatively low nematode densities; in addition the differences

were found dependent on the identity of the nematode species present (De Mesel et al.,

2003; De Mesel et al., 2004; De Mesel et al., 2006). However Hubas et al. (2010)

detected no negative impact on bacterial proliferation in the laboratory experiment at

low nematode densities and suggested that it probably only occur at high abundances of

nematodes with high grazing rates. In our experiments was remarkable an expected

grazing effect for the nematodes densities used, which were comparable to the densities

found in the natural intertidal sediments (Heip et al. 1985), but it would need to test

different densities of bacterivorous nematodes to evaluate the effect of the density.

Besides the effect of bacterivorous nematodes, the effects of predatory

nematodes may be important too for the bacterial activity. In previous laboratory

experiments high predation rates of predacious nematodes have been reported for

marine nematode species such as E. longispiculosus and A. fuscus (Moens et al., 1999;

Moens et al., 2000; Hamels et al., 2001; Gallucci et al., 2005; Moens et al., 2005; dos

Santos & Moens, 2011). Therefore we expected that the predation had a significant

effect on prey.

Trophic-dynamic theories predict that the abundance of a particular trophic level

decrease by the addition of a new level situated an odd number from the given level

(Carpenter et al., 1985; Mikola & Setala, 1998; Laakso & Setala, 1999). Accordingly, in

our experiments we expected that the presence of a predator level would reduce the

biomass of their prey, and that this reduction in turn would partly release bacteria from

grazing. Therefore, the treatments with only prey nematodes and those treatments with

combined prey and predator species were compared.

Particularly in the experiment LM there were no significant differences in β-D-

glucosidase activity between treatments with L. marina and treatments with L. marina

plus predators. In the case of aminopeptidase activity the only significant difference was

found in T4 between treatment BP and treatment BPA, the latter displaying a higher

enzymatic activity. The absence of a cascading effect in the LM experiment could be

38

linked to a lower suitability of L. marina as prey to the predatory nematodes. However,

Moens et al. (2000) still found substantial predation of E. longispiculosus on L. marina,

albeit less than on the monhysterids. In experiment D1, all treatments containing

bacterivorous nematodes had substantially lower β-D-glucosidase activities than the B

treatment or the predator-only treatments. This demonstrates a significant effect of

Diplolaimelloides sp. on bacterial activity, but one that was not counteracted by the

addition of predators. Differences between treatment BP and treatments BPE and BPA

were found only in T1 and T3, the ß-D-glucosidase activity in treatment BP was higher

rather than lower than in the treatments with predators. In contrast, the experiment D2

presented no differences between these treatments. For aminopeptidase activity, in the

experiment D1 no significant differences were found between these treatments, while in

the experiment D2 the treatment BP displayed a lower aminopeptidase activity than

treatments BPE and BPA at all sampling times. The latter data are the only ones

indicating a cascading effect of predators over their prey to the activity of their prey’s

prey. However, given that in this dataset, treatment with bacterivorous nematodes did

not significantly differ from treatment with only bacteria, we cannot really assume that

the predators counteracted an effect of the bacterivorous nematodes. Therefore the

comparisons of treatments regarding both enzymes showed little evidence in support of

trophic cascades.

Our results add to the increasing evidence that propagation of top-down

influences attenuates in the lower levels both in terrestrial (Mikola & Setala, 1998;

Laakso & Setala, 1999) and in aquatic (McQueen et al., 1989) food webs. For instance,

top-down cascades were recorded between piscivores, zooplankton and planktivores in

lakes and there were no evidence for cascading effects between zooplanktivores and

phytoplankton (McQueen et al., 1989). McQueen et al. (1989) using data from

freshwater pelagic ecosystems predicted that the cascading effects in a top-down control

weaken from top towards the bottom. In soil food webs it was observed that the length

of the food chain affected to microbial population when it was measured the

phospholipids fatty acids, respiration and ammonium-N concentration (Mikola &

Setala, 1998). However, appreciable differences between tested chains of two and three

trophic levels were not found, i.e the abundance of bacteria did not respond to the

presence of predator species (Mikola & Setala, 1998), as it is predicted by the trophic-

dynamic theories (Carpenter et al., 1985). In posterior experiments Laakso & Setala

39

(1999) measured respiration rates and biomass of microbivore nematodes and microbes

in a detrital food web. The authors recorded reductions of more than 50% in nematode

respiration and biomass when the predators were present while the reduction in

microbial respiration was only 16%. This suggested that the top-down control is

unlikely to exert a relevant influence on other organisms than their prey.

Several reasons have been given to explain the rare occurrence of cascading

effects. The excretion of nutrients by microbial grazers causes an indirect grazing-

induced stimulation of microbial activity. This supply of nutrients compensates the

effects of direct consumption; therefore it is one of the main mechanisms preventing the

occurrence of trophic cascades until the level of microbes (Laakso & Setala, 1999).

Consequently it is thought that cascading effects may possibly only occur in systems

with plants, as the indirect stimulation of grazing on microbial activity may be damped

by the uptake of nutrients by plants (Laakso & Setala, 1999). In natural decomposer

food webs, Mikola & Setala (1998) suggested that the high turnover rate of fungi and

bacteria loops is one of the main reasons to make the occurrence of trophic cascades

rare and unpredictable. Furthermore the authors pointed the grazing may be focused on

the most palatable microbes releasing to the less palatable species, which could increase

their biomass, thus preventing the trophic cascade. On the other hand specific-species

responses of prey to predation may affect differently to the strength of trophic cascades

and may reduce the cascading effects (Polis & Strong, 1996). However, we do not

dispose of enough information to explain the mechanisms underlying the no occurrence

of trophic cascades in our experiments.

The variability between experiments D1 and D2 were probably derived from the

different rice extract used, as the rice extract factor was the main component of

variation in analyses of aminopeptidase activity and the second main component in

analyses of β-D-glucosidase activity after Time factor. The experiment D2, where we

used the most concentrated rice extract, presented higher β-D-glucosidase activity and

aminopeptidase activity than experiment D1. However the obtained results of β-D-

glucosidase activity from D1 were more concluding than for D2. In turn the results of

aminopeptidase activity from D2 were more meaningful than in D1. Therefore we

concluded that the top-down effects were more pronounced at relatively low β-D-

glucosidase activity while regarding the aminopeptidase activity the cascading effects

were more remarkable at higher microbial activity. Considering this strong substrate

40

effect, we suggested that, probably, bottom-up effects may have occurred in our

experiments, i.e. the nutrient availability influenced the higher trophic levels in the food

web. Resource availability may limit the population or biomass of higher trophic levels

determining community structure and function through both direct and indirect

processes (Posey et al., 1995; Posey et al., 2002). Nevertheless further rice extracts

should be tested using different prey nematode species to confirm this hypothesis.

Further research involving more diverse and complex assemblages are required

to elucidate the mechanisms driving the trophic cascades and to be able to extrapolate

the lab results to natural habitats. These studies should help to understand better the

oceanic top-down control and its interactions with bottom-up control. Ultimately

advances of this nature would facilitate the mitigation and the forecast of effects of

changing oceanic predator abundances, both of which are crucial for the successful

long-term management of oceanic resources (Baum & Worm, 2009).

ACKNOWLEDGEMENTS

My thanks to Professor Moens for putting at my disposal his long experience in

the research and for their assistance in the preparation of this thesis. Thanks also to my

supervisors Tania Bezerra and Xiuquin Wu for their support and guidance, and to all

laboratory technicians of the Department of Marine Biology, whose invaluable

assistance has made possible to conduct this thesis.

41

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