- Email: [email protected]
Benthic macroinvertebrates' vertical distribution in the Tagus estuary (Portugal): The influence of tidal cycle
Estuarine, Coastal and Shelf Science 86 (2010) 580–586
Contents lists available at ScienceDirect
Estuarine, Coastal and Shelf Science journal homepage: www.elsevier.com/locate/ecss
Benthic macroinvertebrates’ vertical distribution in the Tagus estuary (Portugal): The influence of tidal cycle Ineˆs Cardoso a, *, Jose´ Pedro Granadeiro b, Henrique Cabral a, b a b
Centro de Oceanografia, Faculdade de Cieˆncias da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal Centro de Biologia Ambiental, Faculdade de Cieˆncias da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
a r t i c l e i n f o
a b s t r a c t
Article history: Received 2 July 2009 Accepted 20 November 2009 Available online 26 November 2009
In this work we evaluated the vertical distribution pattern of benthic infauna during the tidal cycle at one of the most important mudflats of the Tagus estuary. Samples were collected hourly during 24 h periods at four complete tidal cycles, using a corer specifically designed for the study purpose that allowed easy and effective separation of 15 different sediment layers. A particular case of general linear models, the hurdle model, was used to analyse data sets. We found that different species have different distribution and abundance according to sediment layers. Results showed that individuals tend to go deeper into sediment with a lower water column height and that these migrations are more visible during spring tides. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: benthic macroinvertebrates vertical distribution mudflats tidal cycle Tagus estuary
1. Introduction Macroinvertebrates play a key role in estuarine environments and are partially responsible for the extremely high productivity of these systems (Sarda´ et al., 1998; Rosenberg, 2001; MermillodBlondin et al., 2003). Infauna intertidal communities are dominated by resilient species that tolerate stressful environmental conditions in the sediment profile, where oxygen, light, temperature, salinity, water content, predator exposure and food availability are usually limiting factors that determine the community composition and structure (Armonies, 1988a; Evans et al., 1998). When established, these communities, act on their own environment, altering the sediments physical conditions by enhancing water–sediment fluxes, increasing the area available for solute exchange and the oxic/anoxic surface boundaries by sediment reworking and bioturbation (Rhoads, 1974; Rosenberg, 2001; Gerino et al., 2003, 2007; Mermillod-Blondin and Rosenberg, 2006; Waldbusser and Marinelli, 2006; Volkenborn et al., 2007), and promoting community succession, species settlement and populations persistence (Flint and Kalke, 1986; Schaffner, 1990; Dittmann, 1996; Volkenborn et al., 2007). Benthic macroinvertebrates migrate within the sediment at various time and spatial scales, reacting to a variety of environmental factors. Infauna vertical movements within the sediment,
* Corresponding author. E-mail address: [email protected] (I. Cardoso). 0272-7714/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2009.11.024
particularly those that consist of migration towards the water column (Armonies, 1988b) and subsequent drifting (Armonies, 1992), alter two main aspects in the benthic environment and community structure: the biogenic characteristics and the individuals’ distribution in sediment profile (Mermillod-Blondin et al., 2003). The distribution of infauna in the sediment profile is a direct consequence of the ecological preferences of each species, but it can show seasonal fluctuations due to reproduction and settlement events and also due to species interactions (Flint and Kalke, 1986). Furthermore, species will often change their preferred burrowing depth according to individual dimensions (Esselink and Zwarts, 1989). At small time scales, individuals can move within the sediment as a result of feeding behaviour or to escape from predators (Esselink and Zwarts, 1989; Persson and Svensson, 2006). Laboratory experiments have also shown that some species, when disturbed, can even adjust their vertical distribution by punctual burial events (Armonies, 1988a; Chandrasekara and Frid, 1998). The optimal burrowing depth of infaunal organisms is, therefore, often determined by a trade-off between a higher access to the richer and more oxygenated surface sediments (which would favour a more superficial occurrence) and the increased risks of predations by surface or shallow-feeding predators such as fishes and birds (representing a strong pressure to remain deep in the sediment). In fact, predation pressure has been identified as a major factor determining the average depth of bivalves (Goeij et al., 2001), and this probably also applies to the majority of infaunal species. In areas with a clear tidal regime, the importance of the biological and physical factors determining burrowing depth will
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
change periodically (Cruz-Motta, 2005), and thus we predict that the vertical distribution of many organisms might change accordingly. To test this hypothesis, we examined the tidal variation in the vertical distribution of several species in an estuarine environment. Despite the pioneering account of a tidally synchronised movement of the intertidal flat-worm Convoluta roscoffensis (Gamble and Keeble, 1904), studying such an issue remains an observational challenge. Therefore, to overcome this challenge we used new analytic approaches to further explore these dynamic data.
2. Methods 2.1. Sampling surveys Samples were collected at an intertidal mudflat located in the south margin of the Tagus estuary (38 400 N; 9150 W) (Fig. 1; ‘‘Hortas’’). This is one of the largest estuaries in the Atlantic coast of Europe, with an approximate area of 325 km2, about 40% of which is intertidal mudflat. The mean river flow is 400 m3 s1, being highly variable both seasonally and inter annually. Salinity varies from 0, 50 km upstream from the mouth, to nearly 37 at the mouth of the estuary. Four complete tidal cycles, two during spring tides and two during neap tides, were surveyed for 24 h, in June 2004. During each tidal cycle, 3 sediment cores were collected hourly, with a corer (11 cm of diameter) that allowed sampling collection during both low and high tide in using a small fishing vessel. The corer design allowed it to be opened in two halves, and thus, each core was subdivided in 15 layers immediately after collection. We defined 10 superficial layers of 1 cm and 5 deeper layers of 2 cm and each parcel was separately preserved in 10% formalin, and later sieved through a 500 mm mesh. A total of 2340 sediment slices were analyzed and all the individuals collected in each sample were identified and counted.
581
2.2. Data analysis Two main aspects were determinant for the selection of the data analyses methods: a) data matrices were counts of individuals with a large number of zeros; and b) the variation of counts between sediment layers was to be tested as cycle processes, therefore, not linear. For these main reasons, we used a conditional model, the hurdle model, which is a particular case of General Linear Models (GLM). It was first described by Mullahy (1986) and consists of a zero mass and truncated form of a standard discrete distribution, such as the binomial, Poisson or negative binomial. For our modelling purpose the most adequate distribution was the Poisson. The hurdle model can be interpreted as a two-part model. The first part is typically a binary response model while the second part is usually a zero-truncated count model. This allows us to interpret the positive outcomes (>0) that result from passing hurdle (threshold). The hurdle portion of the two-part model estimates the probability that the threshold is crossed (Rose et al., 2006). As described by Mullahy (1986) the hurdle model can be expressed as (1)
P½y ¼ 0 ¼ f1 ð0Þ ¼ p
(1)
where f1 ($) governs the hurdle part and f2 ($) the count process once the hurdle has been crossed. Furthermore, f1 (0) is the prob0 ability of crossing the hurdle and f2 ($) is the truncated normalization of f2 ($). Hurdle models can be specified in various ways by choosing different distributions for f1 ($) and f2 ($). To model individuals distributions in different layers in the sediment profile we specified the Poisson distribution to characterise f2 ($), where it is assumed that the number of events follows a Poisson distribution with a conditional mean (m) depending upon a set of repressors (x) and corresponding parameters (b) for a linear predictor. Using a log link function we can express the expected number of events for 0 a species i at a sediment layer j as mij ¼ Eðyij jxij Þ ¼ eb xij . The Poisson probability distribution of yij given xij is expressed as (2)
Fig. 1. Location of sampling site in the Tagus estuary.
582
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
Table 1 Results obtained for the hurdle model applied to Ostracoda vertical distribution in the sediment. Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 3.149 Sediment layer 0.669 Water level 0.009 Tide 0.855 Sediment layer:water level 0.014 Water level:tide 0.009 Sediment layer:tide 0.492 Sediment layer:water level:tide 0.014
with log link) 0.217 14.544 0.000 0.179 3.739 0.000 0.005 1.768 0.077 0.220 3.879 0.000 0.004 3.526 0.000 0.005 1.610 0.107 0.179 2.746 0.006 0.004 3.722 0.000
Zero hurdle model coefficients (censored Poisson with log link) Intercept 0.798 0.396 2.017 0.044 Sediment layer 0.433 0.113 3.827 0.000 Water level 0.050 0.016 3.163 0.002 Tide 0.420 0.409 1.027 0.304 Sediment layer:water level 0.020 0.008 2.578 0.010 Water level:tide 0.045 0.016 2.837 0.005 Sediment layer:tide 0.350 0.114 3.078 0.002 Sediment layer:water level:tide 0.018 0.008 2.326 0.020 Log-likelihood
(2)
which results in the Poisson hurdle model for i individuals at layer j being defined as (3)
P Yij ¼ yij
8 > p > < ij emij myij ij ¼ 1 pij > > : 1emij yij !
yij ¼ 0 yij > 0
(3)
The expected value for the Poisson hurdle (PH) model is given by (4)
EðyÞ ¼
ð1 pÞm 1 f2 ð0Þ
(5)
where Y is the abundance of individuals, at a sediment profile layer (SL) with a known water column height (W) and at a particular tide type (T). Predictors SL, W and T were considered significant for a 0.05 level. The analyses were limited to the most abundant taxa, since relationships with rare or occasional species may be strongly biased and/ or independent from tidal cycle. The taxa considered were Ostracoda, Hydrobia ulvae (Gastropoda), Hediste diversicolor (Polychaeta), Scrobicularia plana (Bivalvia) and Cyathura carinata (Isopoda). 3. Results
2574 on 16 df
y emij mijij P Yij ¼ yij ¼ yij !
gðYÞ ¼ a þ b SL þ c T W SL d SL2 þ e T W SL2
(4)
We used the pcsl package of R 2.5.1 software library with log link function to model the vertical distribution of individuals during tidal cycles that can be described by the following regression formula (5):
Overall, our results showed that individuals tend to go deeper into the sediment with a lower water column height and that these migrations are more visible during spring tides. The sensibility of the adjusted model differed among species with different strategies and different distributions within the sediment. The best fitted model was for Ostracoda distribution with a loglikelihood of 2574 on 16 degrees of freedom (df). In this group, densities not only varied among sediment layers but this variation was significantly related with the water column level. Water level alone did not affect Ostracoda densities and differences between spring and neap tides were also observed for densities’ variation. Results from the logistic part of the model also showed that presence/absence of individuals in sediment layers depended not only on depth but also on water column level (Table 1). These results pointed out a possible migration of Ostracoda individuals during tide cycle. In fact, during low tide individuals tend to occur deeper in the sediment profile and density appears to decrease in high water column levels (Fig. 2). One exception was found when water level ¼ 115 cm, where densities achieved their maximum. In neap tides migration patterns of Ostracoda were absent or less evident. For the Gastropoda H. ulvae (Table 2) no migration patterns were apparent. Individuals’ densities tended to remain constant during the tidal cycle, with no statistically significance of the predictor factors. The logistic model suggested differences in the vertical distribution of H. ulvae, with a significant interaction between sediment layer and tide, and, in fact, in spring tides, individuals tended to occur deeper in the sediment. Conversely, in neap tides H. ulvae only occurred in three cycle stages, and thus patterns were more difficult to detect (Fig. 3).
Fig. 2. Vertical distribution of Ostracoda in the sediment (mean density, in individuals m2) according to water column level, during spring (a) and neap (b) tides.
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586 Table 2 Results obtained for the hurdle model applied to Hydrobia ulvae vertical distribution in the sediment.
Table 3 Results obtained for the hurdle model applied to Scrobicularia plana vertical distribution in the sediment.
Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 0.076 Sediment layer 0.198 Water level 0.012 Tide 0.564 Sediment layer:water level 0.003 Water level:tide 0.014 Sediment layer:tide 0.159 Sediment layer:water level:tide 0.003
with log link) 0.606 0.126 0.900 0.201 0.981 0.327 0.021 0.603 0.547 0.668 0.846 0.398 0.006 0.441 0.660 0.021 0.697 0.486 0.205 0.775 0.438 0.006 0.462 0.644
583
Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 1.668 Sediment layer 0.406 Water level 0.005 Tide 0.020 Sediment layer:water level 0.005 Water level:tide 0.007 Sediment layer:tide 0.074 Sediment layer:water level:tide 0.007
with log link) 0.171 9.751 0.000 0.076 5.383 0.000 0.005 1.005 0.315 0.191 0.103 0.918 0.002 2.498 0.012 0.005 1.377 0.170 0.083 0.893 0.372 0.002 3.582 0.000
Zero hurdle model coefficients (censored Poisson with log link) Intercept 0.784 0.345 2.271 0.023 Sediment layer 0.366 0.086 4.279 0.000 Water level 0.007 0.015 0.456 0.648 Tide 0.527 0.377 1.401 0.161 Sediment layer:water level 0.0001 0.004 0.054 0.957 Water level:tide 0.005 0.015 0.353 0.724 Sediment layer:tide 0.256 0.088 2.919 0.004 Sediment layer:water level:tide 0.0002 0.004 0.078 0.938
Zero hurdle model coefficients (censored Poisson with log link) Intercept 0.479 0.197 2.431 0.000 Sediment layer 0.122 0.026 4.771 0.000 Water level 0.0002 0.008 0.037 0.971 Tide 0.151 0.226 0.669 0.504 Sediment layer:water level 0.0001 0.001 0.014 0.989 Water level:tide 0.006 0.008 0.753 0.452 Sediment layer:tide 0.048 0.028 1.681 0.093 Sediment layer:water level:tide 0.012 0.001 1.243 0.214
Log-likelihood
Log-likelihood
677 on 16 df
For S. plana densities, sediment layer and its interaction with water level and tide were significant (Table 3). For presence/ absence analysis the only significant predictor was sediment layer. Individuals of this species have a heterogeneous distribution within sediment layer, with higher densities at the first two superficial centimetres, but with consistent presences in deeper layers as well (Fig. 4). However, this distribution pattern does not seem to be related with water level and vertical movements during tidal cycle. For H. diversicolor, nor densities nor presence/absence data seemed to be related with water column level in each tidal cycle (Table 4). The model identified sediment layer and its interaction with tide (and consequently with water level between cycles) as a relevant predictor of density. The occurrence of H. diversicolor was also related with sediment layer and a significant interaction between layer and the tide level was found. Although no evident tidal migration, our model identified differences in the vertical distribution between spring and neap tides, with individuals being deeper in the sediment in spring tides (Fig. 5). Results for C. carinata were not conclusive with only sediment layer being a significant predictor for the presence and considering
1837 on 16 df
the model’s logistic part (Table 5). Despite the differences found in densities between sampling intervals, water level dependent movements were not detected, probably due to the pattern of horizontal distribution of this species. 4. Discussion The sampling strategy of the present work was directed to in situ observations of tidal variations in species’ vertical distributions during tidal cycle. The main constraints regarded the number of replicates in each sampling period. Because benthic communities are characterized by a clustered distribution (Gerino et al., 2003) it was difficult to sample the horizontal distribution range of species, which can lead to a high variance between replicates. We attempted to overcome this limitation by reducing the number of target species choosing those prevalent in all samples and replicates, allowing continuous observations of individual densities in the sediment profile in all sample periods. As a result we managed to evaluate the variation in the vertical distribution of a diversified group in terms of taxonomic, functional and size range coverage.
Fig. 3. Vertical distribution of Hydrobia ulvae in the sediment (mean density, in individuals m2) according to water column level, during spring (a) and neap (b) tides.
584
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
Fig. 4. Vertical distribution of Scrobicularia plana in the sediment (mean density, in individuals m2) according to water column level, during spring (a) and neap (b) tides.
Infaunal species inhabit a three-dimensional world bounded by the sediment–water interface above and by a range of chemical (e.g. limiting oxygen or sulphide concentrations) and physical (e.g. bedrock) boundaries below (Warwick et al., 2006). The burrowing depth is therefore dependent on the species’ life strategies, their feeding behaviour and their range of tolerance to limiting factors. As a consequence, in all models, it was expected that different species would have different density distributions within the sediment profile. Thus we tested if water column level and tide type, that induce variations on limiting factors, could affect species distributions. The model showed the highest performance for Ostracoda, probably because this was the most prevalent and abundant group. This prevalence and constancy, when compared to the other sampled taxa, is in agreement with Warwick et al.’s (2006) argumentation that horizontal clustering patterns are scaled to animal size. As a result, distribution patterns for smaller individuals can be found within smaller sampling areas. We found evidence of a migration pattern along the tidal cycle, with individuals tending to burrow deeper in sediment in low tide. This behaviour can be interpreted as an escape to surface adverse environmental conditions induced by desiccation. This pattern was stronger in spring
Table 4 Results obtained for the hurdle model applied to Hediste diversicolor vertical distribution in the sediment. Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 0.701 Sediment layer 0.587 Water level 0.062 Tide 2.409 Sediment layer:water level 0.013 Water level:Tide 0.075 Sediment layer:tide 0.666 Sediment layer:water level:tide 0.016
with log link) 0.563 1.245 0.213 0.276 2.125 0.034 0.036 1.731 0.083 0.692 3.484 0.000 0.007 1.928 0.054 0.036 2.054 0.040 0.279 2.389 0.017 0.007 2.291 0.022
Zero hurdle model coefficients (censored Poisson with log link) Intercept 0.730 0.249 2.933 0.003 Sediment layer 0.189 0.041 4.668 0.000 Water level 0.017 0.012 1.369 0.171 Tide 0.744 0.291 2.554 0.011 Sediment layer:water level 0.001 0.002 0.387 0.699 Water level:tide 0.019 0.013 1.519 0.129 Sediment layer:tide 0.100 0.044 2.266 0.023 Sediment layer:water level:tide 0.001 0.002 0.297 0.766 Log-likelihood
789.4 on 16 df
tide, when water levels have more abrupt and contrasting variations. We also observed a persistent density decrease with advancing high tide. These data are in line with observations made under laboratory conditions, where Ostracoda exhibited active emigration from the sediment (Armonies, 1988a,b), resulting in short-term temporal variations in abundance and population structure. This type of migration can be a dispersion strategy to maximize foraging opportunities. The high rates of emergence indicate that there must be a net advantage in regularly leaving the sediment, to compensate for the increased risks of predation. From the ecosystem point of view, emergence from the sediment is regarded as an efficient way of energy exchange between benthonic and pelagical environments (Armonies, 1988a). Similar emergence patterns during tidal cycle have also been described for benthic copepods by Palmer and Brandt (1981), whose results suggested that behaviour plays, indeed, a role in the tidal patterns. No evidence of migratory behaviour was detected in the Gastropoda H. ulvae. Nevertheless, tidal-induced migrations have been reported by Vader (1964) in field and laboratory environments. Active migrations of H. ulvae were also observed by Chandrasekara and Frid (1998) after induced borrow events. When subjected to these stressful events, individuals tend to migrate upwards but the time scale of this migration seems to be out-phased with variations in the water column and in sediment conditions during tidal cycle. Although we detected differences in densities between neap and spring tides, the patterns were not very clear, probably due to the short duration of the neap tide. In addition, H. ulvae is a floating mud snail and, therefore, individuals’ densities and distribution can change at different temporal scales, depending on factors like wind, current and light (Armonies, 1992). The model performed well for S. plana distribution, but we did not find evidence of vertical migration, with no significance of water level related factors at the logistic part of the model. Instead, they enhance size dependent vertical distributions, with smaller individuals higher densities in the superficial layers, and less abundant but deeper in the sediment profile adults of S. plana. This has been described by other authors, being depth a function of siphon size (Zwarts, 1986; Zwarts and Wanink, 1989). For this species our model could detect differences in the vertical distribution not induced by the water column level, which can be explained by specimens’ inherent features. We found no clear distributional patterns or evidence of migratory behaviour for H. diversicolor. Individuals of this species are very active, moving very fast inside their galleries when
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
585
Fig. 5. Vertical distribution of Hediste diversicolor in the sediment (mean density, in individuals m2) according to water column level, during spring (a) and neap (b) tides.
disturbed (Vader, 1964) and therefore, their instantaneous vertical distribution is very difficult to sample accurately. Nevertheless we were able to detect differences between spring and neap tides. Like H. ulvae and Ostracoda, H. diversicolor tended to occur deeper in the sediment in spring tide, when variations on water level conditions are more pronounced. However, more studies are needed to support this for the polychaete, as burrowing depth is also a direct effect of the individuals’ size (Esselink and Zwarts, 1989). The main factors determining the burrowing activity and vertical distribution of H. diversicolor, besides animal size, are the conditions of the sediment (particularly the amount of superficial water) and their feeding strategy (Rosa et al., 2008). In mudflats more deposit feeding is to be expected, as food supplies are concentrated mainly in the sediment surface. Nevertheless, the surface activity of H. diversicolor is minimal during high tide (Vader, 1964; Esselink and Zwarts, 1989) probably to escape the risk of being predated by fish, shrimps and crabs (Esselink and Zwarts, 1989). Although the outputs for C. carinata had no significance on migrations factors, it was interesting to notice that individuals can go down to 7 cm below sediment surface, which is in agreement with Hines and Comtois (1985) observations, and probably indicates that some migration can occur. Here the horizontal distribution may
Table 5 Results obtained for the hurdle model applied to Cyathura carinata vertical distribution in the sediment. Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 0.862 Sediment layer 0.005 Water level 0.038 Tide 0.519 Sediment layer:water level 0.004 Water level:tide 0.042 Sediment layer:tide 0.145 Sediment layer:water level:tide 0.0002
with log link) 0.658 1.310 0.190 0.119 0.044 0.965 0.060 0.631 0.528 0.805 0.644 0.519 0.016 0.245 0.807 0.060 0.703 0.482 0.170 0.854 0.393 0.017 0.016 0.987
Zero hurdle model coefficients (censored Poisson with log link) Intercept 1.056 0.336 3.141 0.002 Sediment layer 0.285 0.069 4.121 0.000 Water level 0.001 0.012 0.044 0.965 Tide 0.100 0.385 0.257 0.797 Sediment layer:water level 0.001 0.002 0.556 0.578 Water level:tide 0.001 0.012 0.038 0.970 Sediment layer:tide 0.045 0.078 0.585 0.556 Sediment layer:water level:tide 0.002 0.002 0.867 0.386 Log-likelihood
489.8 on 16 df
have had an important role towards the non-significance of all migration related factors and interactions (water column level, tide, and their interaction with sediment layer). 5. Conclusions Our results reflected different types of adaptations to the variable environment of tidal mudflat platforms: vertical migration patterns, size dependent distribution and feeding behaviour. Our results could also be reflecting different dispersion strategies and horizontal clustered distribution which added some difficulties to model fitting. Nevertheless, all these characteristics are a direct consequence of species resilience. This allows their persistence, whilst reflecting specimens’ ability to tolerate changes and disturbances and still maintain the same relationships between species or environmental variables (Holling, 1973). This resilience maintains estuarine intertidal environment and benthic systems functional during hourly, daily and seasonal environmental fluctuations. This is reflected by the importance of these systems in estuarine productivity. Although species have survival strategies to avoid predators, in benthic systems vertical distribution of specimens is always a trade-off between poor environmental conditions in the deeper and anoxic layers and surface exposure. More studies with this regard can therefore allow a better understanding of prey availability, during tide variations. This will have practical application in the feeding ecology of predator species. In fact, benthic prey availability is a function of several factors such as water column level, community structure and individuals’ size. Itself, the presence of a potential feeding item in benthic communities doesn’t necessarily be an enhance of its availability to predators. Acknowledgements We thank S. França, R.P. Vasconcelos, P. Santos, V. Fonseca, C. Teixeira, J. Marques, C. Vinagre, F. Baeta, and others for help in field work. This sudy was partial funded by Fundaça˜o para a Cieˆncia e Tecnologia. References Armonies, W., 1988a. Active emergence of meiofauna from intertidal sediment. Marine Ecology Progress Series 43, 151–159. Armonies, W., 1988b. Physical factors influencing active emergence of meiofauna from boreal intertidal sediment. Marine Ecology Progress Series 49, 277–286.
586
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
Armonies, W., 1992. Migratory rhythms of drifting juvenile molluscs in tidal waters of Wadden Sea. Marine Ecology Progress Series 83, 197–206. Chandrasekara, W.U., Frid, C.L.J., 1998. A laboratory assessment of the survival and vertical movement of two epibenthic gastropod species, Hydrobia ulvae (Pennant) and Littorina littorea (Linnaeus), after burial in sediment. Journal of Experimental Marine Biology and Ecology 221, 191–207. Cruz-Motta, J.J., 2005. Diel and tidal variations of benthic assemblages in sediments associated with boulder fields. Marine Ecology Progress Series 290, 97–107. Dittmann, S., 1996. Effect of macrobenthic burrows on infaunal communities in tropical tidal flats. Marine Ecology Progress Series 134, 119–130. Esselink, P., Zwarts, L., 1989. Seasonal trend in burrow depth and tidal variation in feeding activity of Nereis diversicolor. Marine Ecology Progress Series 56, 243–254. Evans, P.R., Ward, R.M., Bone, M., Leakey, M., 1998. Creation of temperate-climate intertidal mudflats: factors affecting colonization and use by benthic invertebrates and their bird predators. Marine Pollution Bulletin 37, 535–545. Flint, R.W., Kalke, R., 1986. Biological enhancement of estuarine benthic community structure. Marine Ecology Progress Series 31, 23–33. Gamble, F.W., Keeble, F., 1904. The bionomics of Convoluta roscoffensis with special reference to its green cells. Quarterly Journal of Microscopical Science 47, 363–431. Gerino, M., Stora, G., François-Carcaillet, F., Gilbert, F., Poggiale, J.C., MermillodBlondin, F., Desrosiers, G., Vervier, P., 2003. Macro-invertebrate functional groups in freshwater and marine sediments: a common mechanistic classification. Vie et Milieu-Life and Environment 53, 221–231. Gerino, M., Frignani, M., Mugnai, C., Belluci, L.G., Prvedelli, D., Valentini, A., Castelli, A., Delmotte, S., Sauvage, S., 2007. Bioturbation in the Venice Lagoon: rates and relationship to organisms. Acta Oecologica 32, 14–25. Goeij, P.de, Luttikhuizen, P.C., Meer, J.Van der, Piersma, T., 2001. Facilitation on an intertidal mudflat: the effect of siphon nipping by flatfish on burying depth of the bivalve Macoma balthica. Oecologia 126, 500–506. Hines, A., Comtois, K., 1985. Vertical distribution of infauna in sediments of a subestuary of central Chesapeake Bay. Estuaries 8, 296–304. Holling, C.S., 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4, 1–23. Mermillod-Blondin, F., Marie, S., Desrosiers, G., Long, B., Montety, L., Michaud, E., Stora, G., 2003. Assessment of the spatial variability of intertidal benthic communities by axial tomodensitometry: importance of fine-scale heterogeneity. Journal of Experimental Marine Biology and Ecology 287, 193–208. Mermillod-Blondin, F., Rosenberg, R., 2006. Ecosystem engineering: the impact of bioturbation on biogeochemical processes in marine and freshwater benthic habitats. Aquatic Science 68, 434–444.
Mullahy, J., 1986. Specification and testing of some modified count data models. Journal of Econometrics 33, 341–365. Palmer, M.A., Brandt, R.R., 1981. Tidal variation in sediment densities of marine benthic copepods. Marine Ecology Progress Series 4, 207–212. Persson, A., Svensson, J.M., 2006. Vertical distribution of benthic community response to fish predators, and effects on algae and suspended material. Aquatic Ecology 40, 85–95. Rhoads, D.C., 1974. Organism–sediment interactions on the muddy seafloor. Oceanography and Marine Biology: An Annual Review 12, 263–300. Rosa, S., Granadeiro, J.P., Cruz, M., Palmeirim, J.M., 2008. Invertebrate prey activity varies along the tidal cycle and depends on sediment drainage: consequences for the foraging behaviour of waders. Journal of Experimental Marine Biology and Ecology 353, 35–44. Rose, C.E., Martin, S.W., Wannemuehler, K.A., Plikaytis, B.D., 2006. On the use of zero-inflated and hurdle models for modelling vaccine adverse event count data. Journal of Biopharmaceutical Statistics 16, 463–481. Rosenberg, R., 2001. Marine benthic faunal successional stages and related sedimentary activity. Scientia Marina 65, 107–119. Sarda´, R., Foreman, K., Werme, C.E., Valiela, I., 1998. The impact of epifaunal predation on the structure of macroinfaunal invertebrate communities of tidal saltmarsh creeks. Estuarine, Coastal and Shelf Science 46, 657–669. Schaffner, L.C., 1990. Small-scale organisms distributions and patterns of species diversity: evidence for positive interactions in an estuarine benthic community. Marine Ecology Progress Series 61, 107–117. Vader, W.J.M., 1964. A preliminary investigation into the reactions of the infauna of the tidal flats to tidal fluctuations in water level. Netherlands Journal of Sea Research 2, 189–222. Volkenborn, N., Hedtkamp, S.I.C., Beusekom, J.E.E., Reise, K., 2007. Effects of bioturbation and bioirrigation by lugworms (Arenicola marina) on physical and chemical sediment properties and implications for intertidal habitat succession. Estuarine, Coastal and Shelf Science 74, 331–343. Waldbusser, G.G., Marinelli, R.L., 2006. Macrofaunal modification of pore water advection: role of species function, species interaction, and kinetics. Marine Ecology Progress Series 311, 217–231. Warwick, R.M., Dashfield, S.L., Somerfield, P.J., 2006. The integral structure of a benthic infaunal assemblage. Journal of Experimental Marine Biology and Ecology 330, 12–18. Zwarts, L., 1986. Burying depth of the benthic bivalve Scrobicularia plana (Da Costa) in reaction to siphon-cropping. Journal of Experimental Marine Biology and Ecology 101, 25–39. Zwarts, L., Wanink, J., 1989. Siphon size and borrowing depth in deposit-feeding and suspension-feeding benthic bivalves. Marine Biology 100, 227–240.
Contents lists available at ScienceDirect
Estuarine, Coastal and Shelf Science journal homepage: www.elsevier.com/locate/ecss
Benthic macroinvertebrates’ vertical distribution in the Tagus estuary (Portugal): The influence of tidal cycle Ineˆs Cardoso a, *, Jose´ Pedro Granadeiro b, Henrique Cabral a, b a b
Centro de Oceanografia, Faculdade de Cieˆncias da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal Centro de Biologia Ambiental, Faculdade de Cieˆncias da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
a r t i c l e i n f o
a b s t r a c t
Article history: Received 2 July 2009 Accepted 20 November 2009 Available online 26 November 2009
In this work we evaluated the vertical distribution pattern of benthic infauna during the tidal cycle at one of the most important mudflats of the Tagus estuary. Samples were collected hourly during 24 h periods at four complete tidal cycles, using a corer specifically designed for the study purpose that allowed easy and effective separation of 15 different sediment layers. A particular case of general linear models, the hurdle model, was used to analyse data sets. We found that different species have different distribution and abundance according to sediment layers. Results showed that individuals tend to go deeper into sediment with a lower water column height and that these migrations are more visible during spring tides. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: benthic macroinvertebrates vertical distribution mudflats tidal cycle Tagus estuary
1. Introduction Macroinvertebrates play a key role in estuarine environments and are partially responsible for the extremely high productivity of these systems (Sarda´ et al., 1998; Rosenberg, 2001; MermillodBlondin et al., 2003). Infauna intertidal communities are dominated by resilient species that tolerate stressful environmental conditions in the sediment profile, where oxygen, light, temperature, salinity, water content, predator exposure and food availability are usually limiting factors that determine the community composition and structure (Armonies, 1988a; Evans et al., 1998). When established, these communities, act on their own environment, altering the sediments physical conditions by enhancing water–sediment fluxes, increasing the area available for solute exchange and the oxic/anoxic surface boundaries by sediment reworking and bioturbation (Rhoads, 1974; Rosenberg, 2001; Gerino et al., 2003, 2007; Mermillod-Blondin and Rosenberg, 2006; Waldbusser and Marinelli, 2006; Volkenborn et al., 2007), and promoting community succession, species settlement and populations persistence (Flint and Kalke, 1986; Schaffner, 1990; Dittmann, 1996; Volkenborn et al., 2007). Benthic macroinvertebrates migrate within the sediment at various time and spatial scales, reacting to a variety of environmental factors. Infauna vertical movements within the sediment,
* Corresponding author. E-mail address: [email protected] (I. Cardoso). 0272-7714/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2009.11.024
particularly those that consist of migration towards the water column (Armonies, 1988b) and subsequent drifting (Armonies, 1992), alter two main aspects in the benthic environment and community structure: the biogenic characteristics and the individuals’ distribution in sediment profile (Mermillod-Blondin et al., 2003). The distribution of infauna in the sediment profile is a direct consequence of the ecological preferences of each species, but it can show seasonal fluctuations due to reproduction and settlement events and also due to species interactions (Flint and Kalke, 1986). Furthermore, species will often change their preferred burrowing depth according to individual dimensions (Esselink and Zwarts, 1989). At small time scales, individuals can move within the sediment as a result of feeding behaviour or to escape from predators (Esselink and Zwarts, 1989; Persson and Svensson, 2006). Laboratory experiments have also shown that some species, when disturbed, can even adjust their vertical distribution by punctual burial events (Armonies, 1988a; Chandrasekara and Frid, 1998). The optimal burrowing depth of infaunal organisms is, therefore, often determined by a trade-off between a higher access to the richer and more oxygenated surface sediments (which would favour a more superficial occurrence) and the increased risks of predations by surface or shallow-feeding predators such as fishes and birds (representing a strong pressure to remain deep in the sediment). In fact, predation pressure has been identified as a major factor determining the average depth of bivalves (Goeij et al., 2001), and this probably also applies to the majority of infaunal species. In areas with a clear tidal regime, the importance of the biological and physical factors determining burrowing depth will
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
change periodically (Cruz-Motta, 2005), and thus we predict that the vertical distribution of many organisms might change accordingly. To test this hypothesis, we examined the tidal variation in the vertical distribution of several species in an estuarine environment. Despite the pioneering account of a tidally synchronised movement of the intertidal flat-worm Convoluta roscoffensis (Gamble and Keeble, 1904), studying such an issue remains an observational challenge. Therefore, to overcome this challenge we used new analytic approaches to further explore these dynamic data.
2. Methods 2.1. Sampling surveys Samples were collected at an intertidal mudflat located in the south margin of the Tagus estuary (38 400 N; 9150 W) (Fig. 1; ‘‘Hortas’’). This is one of the largest estuaries in the Atlantic coast of Europe, with an approximate area of 325 km2, about 40% of which is intertidal mudflat. The mean river flow is 400 m3 s1, being highly variable both seasonally and inter annually. Salinity varies from 0, 50 km upstream from the mouth, to nearly 37 at the mouth of the estuary. Four complete tidal cycles, two during spring tides and two during neap tides, were surveyed for 24 h, in June 2004. During each tidal cycle, 3 sediment cores were collected hourly, with a corer (11 cm of diameter) that allowed sampling collection during both low and high tide in using a small fishing vessel. The corer design allowed it to be opened in two halves, and thus, each core was subdivided in 15 layers immediately after collection. We defined 10 superficial layers of 1 cm and 5 deeper layers of 2 cm and each parcel was separately preserved in 10% formalin, and later sieved through a 500 mm mesh. A total of 2340 sediment slices were analyzed and all the individuals collected in each sample were identified and counted.
581
2.2. Data analysis Two main aspects were determinant for the selection of the data analyses methods: a) data matrices were counts of individuals with a large number of zeros; and b) the variation of counts between sediment layers was to be tested as cycle processes, therefore, not linear. For these main reasons, we used a conditional model, the hurdle model, which is a particular case of General Linear Models (GLM). It was first described by Mullahy (1986) and consists of a zero mass and truncated form of a standard discrete distribution, such as the binomial, Poisson or negative binomial. For our modelling purpose the most adequate distribution was the Poisson. The hurdle model can be interpreted as a two-part model. The first part is typically a binary response model while the second part is usually a zero-truncated count model. This allows us to interpret the positive outcomes (>0) that result from passing hurdle (threshold). The hurdle portion of the two-part model estimates the probability that the threshold is crossed (Rose et al., 2006). As described by Mullahy (1986) the hurdle model can be expressed as (1)
P½y ¼ 0 ¼ f1 ð0Þ ¼ p
(1)
where f1 ($) governs the hurdle part and f2 ($) the count process once the hurdle has been crossed. Furthermore, f1 (0) is the prob0 ability of crossing the hurdle and f2 ($) is the truncated normalization of f2 ($). Hurdle models can be specified in various ways by choosing different distributions for f1 ($) and f2 ($). To model individuals distributions in different layers in the sediment profile we specified the Poisson distribution to characterise f2 ($), where it is assumed that the number of events follows a Poisson distribution with a conditional mean (m) depending upon a set of repressors (x) and corresponding parameters (b) for a linear predictor. Using a log link function we can express the expected number of events for 0 a species i at a sediment layer j as mij ¼ Eðyij jxij Þ ¼ eb xij . The Poisson probability distribution of yij given xij is expressed as (2)
Fig. 1. Location of sampling site in the Tagus estuary.
582
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
Table 1 Results obtained for the hurdle model applied to Ostracoda vertical distribution in the sediment. Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 3.149 Sediment layer 0.669 Water level 0.009 Tide 0.855 Sediment layer:water level 0.014 Water level:tide 0.009 Sediment layer:tide 0.492 Sediment layer:water level:tide 0.014
with log link) 0.217 14.544 0.000 0.179 3.739 0.000 0.005 1.768 0.077 0.220 3.879 0.000 0.004 3.526 0.000 0.005 1.610 0.107 0.179 2.746 0.006 0.004 3.722 0.000
Zero hurdle model coefficients (censored Poisson with log link) Intercept 0.798 0.396 2.017 0.044 Sediment layer 0.433 0.113 3.827 0.000 Water level 0.050 0.016 3.163 0.002 Tide 0.420 0.409 1.027 0.304 Sediment layer:water level 0.020 0.008 2.578 0.010 Water level:tide 0.045 0.016 2.837 0.005 Sediment layer:tide 0.350 0.114 3.078 0.002 Sediment layer:water level:tide 0.018 0.008 2.326 0.020 Log-likelihood
(2)
which results in the Poisson hurdle model for i individuals at layer j being defined as (3)
P Yij ¼ yij
8 > p > < ij emij myij ij ¼ 1 pij > > : 1emij yij !
yij ¼ 0 yij > 0
(3)
The expected value for the Poisson hurdle (PH) model is given by (4)
EðyÞ ¼
ð1 pÞm 1 f2 ð0Þ
(5)
where Y is the abundance of individuals, at a sediment profile layer (SL) with a known water column height (W) and at a particular tide type (T). Predictors SL, W and T were considered significant for a 0.05 level. The analyses were limited to the most abundant taxa, since relationships with rare or occasional species may be strongly biased and/ or independent from tidal cycle. The taxa considered were Ostracoda, Hydrobia ulvae (Gastropoda), Hediste diversicolor (Polychaeta), Scrobicularia plana (Bivalvia) and Cyathura carinata (Isopoda). 3. Results
2574 on 16 df
y emij mijij P Yij ¼ yij ¼ yij !
gðYÞ ¼ a þ b SL þ c T W SL d SL2 þ e T W SL2
(4)
We used the pcsl package of R 2.5.1 software library with log link function to model the vertical distribution of individuals during tidal cycles that can be described by the following regression formula (5):
Overall, our results showed that individuals tend to go deeper into the sediment with a lower water column height and that these migrations are more visible during spring tides. The sensibility of the adjusted model differed among species with different strategies and different distributions within the sediment. The best fitted model was for Ostracoda distribution with a loglikelihood of 2574 on 16 degrees of freedom (df). In this group, densities not only varied among sediment layers but this variation was significantly related with the water column level. Water level alone did not affect Ostracoda densities and differences between spring and neap tides were also observed for densities’ variation. Results from the logistic part of the model also showed that presence/absence of individuals in sediment layers depended not only on depth but also on water column level (Table 1). These results pointed out a possible migration of Ostracoda individuals during tide cycle. In fact, during low tide individuals tend to occur deeper in the sediment profile and density appears to decrease in high water column levels (Fig. 2). One exception was found when water level ¼ 115 cm, where densities achieved their maximum. In neap tides migration patterns of Ostracoda were absent or less evident. For the Gastropoda H. ulvae (Table 2) no migration patterns were apparent. Individuals’ densities tended to remain constant during the tidal cycle, with no statistically significance of the predictor factors. The logistic model suggested differences in the vertical distribution of H. ulvae, with a significant interaction between sediment layer and tide, and, in fact, in spring tides, individuals tended to occur deeper in the sediment. Conversely, in neap tides H. ulvae only occurred in three cycle stages, and thus patterns were more difficult to detect (Fig. 3).
Fig. 2. Vertical distribution of Ostracoda in the sediment (mean density, in individuals m2) according to water column level, during spring (a) and neap (b) tides.
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586 Table 2 Results obtained for the hurdle model applied to Hydrobia ulvae vertical distribution in the sediment.
Table 3 Results obtained for the hurdle model applied to Scrobicularia plana vertical distribution in the sediment.
Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 0.076 Sediment layer 0.198 Water level 0.012 Tide 0.564 Sediment layer:water level 0.003 Water level:tide 0.014 Sediment layer:tide 0.159 Sediment layer:water level:tide 0.003
with log link) 0.606 0.126 0.900 0.201 0.981 0.327 0.021 0.603 0.547 0.668 0.846 0.398 0.006 0.441 0.660 0.021 0.697 0.486 0.205 0.775 0.438 0.006 0.462 0.644
583
Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 1.668 Sediment layer 0.406 Water level 0.005 Tide 0.020 Sediment layer:water level 0.005 Water level:tide 0.007 Sediment layer:tide 0.074 Sediment layer:water level:tide 0.007
with log link) 0.171 9.751 0.000 0.076 5.383 0.000 0.005 1.005 0.315 0.191 0.103 0.918 0.002 2.498 0.012 0.005 1.377 0.170 0.083 0.893 0.372 0.002 3.582 0.000
Zero hurdle model coefficients (censored Poisson with log link) Intercept 0.784 0.345 2.271 0.023 Sediment layer 0.366 0.086 4.279 0.000 Water level 0.007 0.015 0.456 0.648 Tide 0.527 0.377 1.401 0.161 Sediment layer:water level 0.0001 0.004 0.054 0.957 Water level:tide 0.005 0.015 0.353 0.724 Sediment layer:tide 0.256 0.088 2.919 0.004 Sediment layer:water level:tide 0.0002 0.004 0.078 0.938
Zero hurdle model coefficients (censored Poisson with log link) Intercept 0.479 0.197 2.431 0.000 Sediment layer 0.122 0.026 4.771 0.000 Water level 0.0002 0.008 0.037 0.971 Tide 0.151 0.226 0.669 0.504 Sediment layer:water level 0.0001 0.001 0.014 0.989 Water level:tide 0.006 0.008 0.753 0.452 Sediment layer:tide 0.048 0.028 1.681 0.093 Sediment layer:water level:tide 0.012 0.001 1.243 0.214
Log-likelihood
Log-likelihood
677 on 16 df
For S. plana densities, sediment layer and its interaction with water level and tide were significant (Table 3). For presence/ absence analysis the only significant predictor was sediment layer. Individuals of this species have a heterogeneous distribution within sediment layer, with higher densities at the first two superficial centimetres, but with consistent presences in deeper layers as well (Fig. 4). However, this distribution pattern does not seem to be related with water level and vertical movements during tidal cycle. For H. diversicolor, nor densities nor presence/absence data seemed to be related with water column level in each tidal cycle (Table 4). The model identified sediment layer and its interaction with tide (and consequently with water level between cycles) as a relevant predictor of density. The occurrence of H. diversicolor was also related with sediment layer and a significant interaction between layer and the tide level was found. Although no evident tidal migration, our model identified differences in the vertical distribution between spring and neap tides, with individuals being deeper in the sediment in spring tides (Fig. 5). Results for C. carinata were not conclusive with only sediment layer being a significant predictor for the presence and considering
1837 on 16 df
the model’s logistic part (Table 5). Despite the differences found in densities between sampling intervals, water level dependent movements were not detected, probably due to the pattern of horizontal distribution of this species. 4. Discussion The sampling strategy of the present work was directed to in situ observations of tidal variations in species’ vertical distributions during tidal cycle. The main constraints regarded the number of replicates in each sampling period. Because benthic communities are characterized by a clustered distribution (Gerino et al., 2003) it was difficult to sample the horizontal distribution range of species, which can lead to a high variance between replicates. We attempted to overcome this limitation by reducing the number of target species choosing those prevalent in all samples and replicates, allowing continuous observations of individual densities in the sediment profile in all sample periods. As a result we managed to evaluate the variation in the vertical distribution of a diversified group in terms of taxonomic, functional and size range coverage.
Fig. 3. Vertical distribution of Hydrobia ulvae in the sediment (mean density, in individuals m2) according to water column level, during spring (a) and neap (b) tides.
584
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
Fig. 4. Vertical distribution of Scrobicularia plana in the sediment (mean density, in individuals m2) according to water column level, during spring (a) and neap (b) tides.
Infaunal species inhabit a three-dimensional world bounded by the sediment–water interface above and by a range of chemical (e.g. limiting oxygen or sulphide concentrations) and physical (e.g. bedrock) boundaries below (Warwick et al., 2006). The burrowing depth is therefore dependent on the species’ life strategies, their feeding behaviour and their range of tolerance to limiting factors. As a consequence, in all models, it was expected that different species would have different density distributions within the sediment profile. Thus we tested if water column level and tide type, that induce variations on limiting factors, could affect species distributions. The model showed the highest performance for Ostracoda, probably because this was the most prevalent and abundant group. This prevalence and constancy, when compared to the other sampled taxa, is in agreement with Warwick et al.’s (2006) argumentation that horizontal clustering patterns are scaled to animal size. As a result, distribution patterns for smaller individuals can be found within smaller sampling areas. We found evidence of a migration pattern along the tidal cycle, with individuals tending to burrow deeper in sediment in low tide. This behaviour can be interpreted as an escape to surface adverse environmental conditions induced by desiccation. This pattern was stronger in spring
Table 4 Results obtained for the hurdle model applied to Hediste diversicolor vertical distribution in the sediment. Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 0.701 Sediment layer 0.587 Water level 0.062 Tide 2.409 Sediment layer:water level 0.013 Water level:Tide 0.075 Sediment layer:tide 0.666 Sediment layer:water level:tide 0.016
with log link) 0.563 1.245 0.213 0.276 2.125 0.034 0.036 1.731 0.083 0.692 3.484 0.000 0.007 1.928 0.054 0.036 2.054 0.040 0.279 2.389 0.017 0.007 2.291 0.022
Zero hurdle model coefficients (censored Poisson with log link) Intercept 0.730 0.249 2.933 0.003 Sediment layer 0.189 0.041 4.668 0.000 Water level 0.017 0.012 1.369 0.171 Tide 0.744 0.291 2.554 0.011 Sediment layer:water level 0.001 0.002 0.387 0.699 Water level:tide 0.019 0.013 1.519 0.129 Sediment layer:tide 0.100 0.044 2.266 0.023 Sediment layer:water level:tide 0.001 0.002 0.297 0.766 Log-likelihood
789.4 on 16 df
tide, when water levels have more abrupt and contrasting variations. We also observed a persistent density decrease with advancing high tide. These data are in line with observations made under laboratory conditions, where Ostracoda exhibited active emigration from the sediment (Armonies, 1988a,b), resulting in short-term temporal variations in abundance and population structure. This type of migration can be a dispersion strategy to maximize foraging opportunities. The high rates of emergence indicate that there must be a net advantage in regularly leaving the sediment, to compensate for the increased risks of predation. From the ecosystem point of view, emergence from the sediment is regarded as an efficient way of energy exchange between benthonic and pelagical environments (Armonies, 1988a). Similar emergence patterns during tidal cycle have also been described for benthic copepods by Palmer and Brandt (1981), whose results suggested that behaviour plays, indeed, a role in the tidal patterns. No evidence of migratory behaviour was detected in the Gastropoda H. ulvae. Nevertheless, tidal-induced migrations have been reported by Vader (1964) in field and laboratory environments. Active migrations of H. ulvae were also observed by Chandrasekara and Frid (1998) after induced borrow events. When subjected to these stressful events, individuals tend to migrate upwards but the time scale of this migration seems to be out-phased with variations in the water column and in sediment conditions during tidal cycle. Although we detected differences in densities between neap and spring tides, the patterns were not very clear, probably due to the short duration of the neap tide. In addition, H. ulvae is a floating mud snail and, therefore, individuals’ densities and distribution can change at different temporal scales, depending on factors like wind, current and light (Armonies, 1992). The model performed well for S. plana distribution, but we did not find evidence of vertical migration, with no significance of water level related factors at the logistic part of the model. Instead, they enhance size dependent vertical distributions, with smaller individuals higher densities in the superficial layers, and less abundant but deeper in the sediment profile adults of S. plana. This has been described by other authors, being depth a function of siphon size (Zwarts, 1986; Zwarts and Wanink, 1989). For this species our model could detect differences in the vertical distribution not induced by the water column level, which can be explained by specimens’ inherent features. We found no clear distributional patterns or evidence of migratory behaviour for H. diversicolor. Individuals of this species are very active, moving very fast inside their galleries when
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
585
Fig. 5. Vertical distribution of Hediste diversicolor in the sediment (mean density, in individuals m2) according to water column level, during spring (a) and neap (b) tides.
disturbed (Vader, 1964) and therefore, their instantaneous vertical distribution is very difficult to sample accurately. Nevertheless we were able to detect differences between spring and neap tides. Like H. ulvae and Ostracoda, H. diversicolor tended to occur deeper in the sediment in spring tide, when variations on water level conditions are more pronounced. However, more studies are needed to support this for the polychaete, as burrowing depth is also a direct effect of the individuals’ size (Esselink and Zwarts, 1989). The main factors determining the burrowing activity and vertical distribution of H. diversicolor, besides animal size, are the conditions of the sediment (particularly the amount of superficial water) and their feeding strategy (Rosa et al., 2008). In mudflats more deposit feeding is to be expected, as food supplies are concentrated mainly in the sediment surface. Nevertheless, the surface activity of H. diversicolor is minimal during high tide (Vader, 1964; Esselink and Zwarts, 1989) probably to escape the risk of being predated by fish, shrimps and crabs (Esselink and Zwarts, 1989). Although the outputs for C. carinata had no significance on migrations factors, it was interesting to notice that individuals can go down to 7 cm below sediment surface, which is in agreement with Hines and Comtois (1985) observations, and probably indicates that some migration can occur. Here the horizontal distribution may
Table 5 Results obtained for the hurdle model applied to Cyathura carinata vertical distribution in the sediment. Estimate Std. error Z value Pr(>jzj) Count model coefficients (truncated Poisson Intercept 0.862 Sediment layer 0.005 Water level 0.038 Tide 0.519 Sediment layer:water level 0.004 Water level:tide 0.042 Sediment layer:tide 0.145 Sediment layer:water level:tide 0.0002
with log link) 0.658 1.310 0.190 0.119 0.044 0.965 0.060 0.631 0.528 0.805 0.644 0.519 0.016 0.245 0.807 0.060 0.703 0.482 0.170 0.854 0.393 0.017 0.016 0.987
Zero hurdle model coefficients (censored Poisson with log link) Intercept 1.056 0.336 3.141 0.002 Sediment layer 0.285 0.069 4.121 0.000 Water level 0.001 0.012 0.044 0.965 Tide 0.100 0.385 0.257 0.797 Sediment layer:water level 0.001 0.002 0.556 0.578 Water level:tide 0.001 0.012 0.038 0.970 Sediment layer:tide 0.045 0.078 0.585 0.556 Sediment layer:water level:tide 0.002 0.002 0.867 0.386 Log-likelihood
489.8 on 16 df
have had an important role towards the non-significance of all migration related factors and interactions (water column level, tide, and their interaction with sediment layer). 5. Conclusions Our results reflected different types of adaptations to the variable environment of tidal mudflat platforms: vertical migration patterns, size dependent distribution and feeding behaviour. Our results could also be reflecting different dispersion strategies and horizontal clustered distribution which added some difficulties to model fitting. Nevertheless, all these characteristics are a direct consequence of species resilience. This allows their persistence, whilst reflecting specimens’ ability to tolerate changes and disturbances and still maintain the same relationships between species or environmental variables (Holling, 1973). This resilience maintains estuarine intertidal environment and benthic systems functional during hourly, daily and seasonal environmental fluctuations. This is reflected by the importance of these systems in estuarine productivity. Although species have survival strategies to avoid predators, in benthic systems vertical distribution of specimens is always a trade-off between poor environmental conditions in the deeper and anoxic layers and surface exposure. More studies with this regard can therefore allow a better understanding of prey availability, during tide variations. This will have practical application in the feeding ecology of predator species. In fact, benthic prey availability is a function of several factors such as water column level, community structure and individuals’ size. Itself, the presence of a potential feeding item in benthic communities doesn’t necessarily be an enhance of its availability to predators. Acknowledgements We thank S. França, R.P. Vasconcelos, P. Santos, V. Fonseca, C. Teixeira, J. Marques, C. Vinagre, F. Baeta, and others for help in field work. This sudy was partial funded by Fundaça˜o para a Cieˆncia e Tecnologia. References Armonies, W., 1988a. Active emergence of meiofauna from intertidal sediment. Marine Ecology Progress Series 43, 151–159. Armonies, W., 1988b. Physical factors influencing active emergence of meiofauna from boreal intertidal sediment. Marine Ecology Progress Series 49, 277–286.
586
I. Cardoso et al. / Estuarine, Coastal and Shelf Science 86 (2010) 580–586
Armonies, W., 1992. Migratory rhythms of drifting juvenile molluscs in tidal waters of Wadden Sea. Marine Ecology Progress Series 83, 197–206. Chandrasekara, W.U., Frid, C.L.J., 1998. A laboratory assessment of the survival and vertical movement of two epibenthic gastropod species, Hydrobia ulvae (Pennant) and Littorina littorea (Linnaeus), after burial in sediment. Journal of Experimental Marine Biology and Ecology 221, 191–207. Cruz-Motta, J.J., 2005. Diel and tidal variations of benthic assemblages in sediments associated with boulder fields. Marine Ecology Progress Series 290, 97–107. Dittmann, S., 1996. Effect of macrobenthic burrows on infaunal communities in tropical tidal flats. Marine Ecology Progress Series 134, 119–130. Esselink, P., Zwarts, L., 1989. Seasonal trend in burrow depth and tidal variation in feeding activity of Nereis diversicolor. Marine Ecology Progress Series 56, 243–254. Evans, P.R., Ward, R.M., Bone, M., Leakey, M., 1998. Creation of temperate-climate intertidal mudflats: factors affecting colonization and use by benthic invertebrates and their bird predators. Marine Pollution Bulletin 37, 535–545. Flint, R.W., Kalke, R., 1986. Biological enhancement of estuarine benthic community structure. Marine Ecology Progress Series 31, 23–33. Gamble, F.W., Keeble, F., 1904. The bionomics of Convoluta roscoffensis with special reference to its green cells. Quarterly Journal of Microscopical Science 47, 363–431. Gerino, M., Stora, G., François-Carcaillet, F., Gilbert, F., Poggiale, J.C., MermillodBlondin, F., Desrosiers, G., Vervier, P., 2003. Macro-invertebrate functional groups in freshwater and marine sediments: a common mechanistic classification. Vie et Milieu-Life and Environment 53, 221–231. Gerino, M., Frignani, M., Mugnai, C., Belluci, L.G., Prvedelli, D., Valentini, A., Castelli, A., Delmotte, S., Sauvage, S., 2007. Bioturbation in the Venice Lagoon: rates and relationship to organisms. Acta Oecologica 32, 14–25. Goeij, P.de, Luttikhuizen, P.C., Meer, J.Van der, Piersma, T., 2001. Facilitation on an intertidal mudflat: the effect of siphon nipping by flatfish on burying depth of the bivalve Macoma balthica. Oecologia 126, 500–506. Hines, A., Comtois, K., 1985. Vertical distribution of infauna in sediments of a subestuary of central Chesapeake Bay. Estuaries 8, 296–304. Holling, C.S., 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4, 1–23. Mermillod-Blondin, F., Marie, S., Desrosiers, G., Long, B., Montety, L., Michaud, E., Stora, G., 2003. Assessment of the spatial variability of intertidal benthic communities by axial tomodensitometry: importance of fine-scale heterogeneity. Journal of Experimental Marine Biology and Ecology 287, 193–208. Mermillod-Blondin, F., Rosenberg, R., 2006. Ecosystem engineering: the impact of bioturbation on biogeochemical processes in marine and freshwater benthic habitats. Aquatic Science 68, 434–444.
Mullahy, J., 1986. Specification and testing of some modified count data models. Journal of Econometrics 33, 341–365. Palmer, M.A., Brandt, R.R., 1981. Tidal variation in sediment densities of marine benthic copepods. Marine Ecology Progress Series 4, 207–212. Persson, A., Svensson, J.M., 2006. Vertical distribution of benthic community response to fish predators, and effects on algae and suspended material. Aquatic Ecology 40, 85–95. Rhoads, D.C., 1974. Organism–sediment interactions on the muddy seafloor. Oceanography and Marine Biology: An Annual Review 12, 263–300. Rosa, S., Granadeiro, J.P., Cruz, M., Palmeirim, J.M., 2008. Invertebrate prey activity varies along the tidal cycle and depends on sediment drainage: consequences for the foraging behaviour of waders. Journal of Experimental Marine Biology and Ecology 353, 35–44. Rose, C.E., Martin, S.W., Wannemuehler, K.A., Plikaytis, B.D., 2006. On the use of zero-inflated and hurdle models for modelling vaccine adverse event count data. Journal of Biopharmaceutical Statistics 16, 463–481. Rosenberg, R., 2001. Marine benthic faunal successional stages and related sedimentary activity. Scientia Marina 65, 107–119. Sarda´, R., Foreman, K., Werme, C.E., Valiela, I., 1998. The impact of epifaunal predation on the structure of macroinfaunal invertebrate communities of tidal saltmarsh creeks. Estuarine, Coastal and Shelf Science 46, 657–669. Schaffner, L.C., 1990. Small-scale organisms distributions and patterns of species diversity: evidence for positive interactions in an estuarine benthic community. Marine Ecology Progress Series 61, 107–117. Vader, W.J.M., 1964. A preliminary investigation into the reactions of the infauna of the tidal flats to tidal fluctuations in water level. Netherlands Journal of Sea Research 2, 189–222. Volkenborn, N., Hedtkamp, S.I.C., Beusekom, J.E.E., Reise, K., 2007. Effects of bioturbation and bioirrigation by lugworms (Arenicola marina) on physical and chemical sediment properties and implications for intertidal habitat succession. Estuarine, Coastal and Shelf Science 74, 331–343. Waldbusser, G.G., Marinelli, R.L., 2006. Macrofaunal modification of pore water advection: role of species function, species interaction, and kinetics. Marine Ecology Progress Series 311, 217–231. Warwick, R.M., Dashfield, S.L., Somerfield, P.J., 2006. The integral structure of a benthic infaunal assemblage. Journal of Experimental Marine Biology and Ecology 330, 12–18. Zwarts, L., 1986. Burying depth of the benthic bivalve Scrobicularia plana (Da Costa) in reaction to siphon-cropping. Journal of Experimental Marine Biology and Ecology 101, 25–39. Zwarts, L., Wanink, J., 1989. Siphon size and borrowing depth in deposit-feeding and suspension-feeding benthic bivalves. Marine Biology 100, 227–240.