CSIRO PUBLISHING
Marine and Freshwater Research, 2015, 66, 170–176 http://dx.doi.org/10.1071/MF14030
Structural complexity and turbidity do not interact to influence predation rate and prey selectivity by a small visually feeding fish Bruno R. S. Figueiredo A,C, Roger P. Mormul A,B and Evanilde Benedito A,B A
Programa de Po´s-graduac¸a˜o em Ecologia de Ambientes Aqua´ticos Continentais, Nu´cleo de Pesquisa em Limnologia, Ictiologia e Aquicultura (Nupe´lia), Universidade Estadual de Maringa´, Avenida Colombo, 5790, Bloco H-90, Maringa´, PR, CEP 87020-900, Brazil. B Programa de Po´s-graduac¸a˜o em Biologia Comparada, Universidade Estadual de Maringa´, Avenida Colombo, 5790, Bloco G-80, Maringa´, PR, CEP 87020-900, Brazil. C Corresponding author. Email:
[email protected]
Abstract. Structural complexity and turbidity decrease predation by respectively providing a physical and visual refuge for prey. It is still unclear how the covariance between these variables could drive predation and prey selectivity. We experimentally simulated scenarios that are temporally observed in floodplain rivers. In the experiments, we crossed different prey types, structural complexity and turbidity. We hypothesised that the negative relationship between structural complexity and predation would become stronger with a linear increase in the turbidity level and that an increase in structural complexity and in turbidity would change prey selectivity from a selective to a random pattern. Our results showed that the effects of structural complexity and turbidity on predation may not covary; a linear increase in turbidity did not significantly change the patterns of predation or prey selectivity. In contrast, structural complexity significantly reduced prey consumption according to prey size. We argue that areas with low macrophyte cover may provide an efficient refuge for smaller prey, whereas an efficient refuge for larger prey can be attained only in areas with high macrophyte cover. In highly complex habitats, specificity in prey consumption is precluded because both prey species can hide amid the interstices of the macrophytes, leading to random prey selectivity. Additional keywords: environmental shifts, invertivorous, predator–prey interaction, submerged macrophytes, visual predation. Received 1 October 2013, accepted 3 June 2014, published online 14 October 2014
Introduction Temporal changes in structural complexity and turbidity often co-occur in ecosystems such as floodplains (e.g. LoverdeOliveira et al. 2009; Mormul et al. 2012). In large floodplain rivers such as the Parana´ River in South America, the amount of suspended sediment entering the rivers increases during the rainy season, and this process limits the availability of underwater light, a fundamental resource for submerged macrophytes (e.g. Egeria najas) that promote habitat complexity (Savino and Stein 1982; Figueiredo et al. 2013). Nevertheless, these plants persist for a short period (Sousa et al. 2010), creating a combination of increased habitat complexity and turbidity in this floodplain system. In contrast, during the dry season, the water gradually becomes clearer, and as submerged vegetation is still absent, the river has high transparency and a simplified habitat for a short period. In a floodplain, such changes can also be caused by shallowing, which tends to transform relatively clear complex habitats into turbid simple ones (e.g. Loverde-Oliveira et al. 2009; Miranda 2011; Mormul et al. 2012). Journal compilation Ó CSIRO 2015
In Neotropical aquatic systems, the ichthyofauna is primarily composed of small fishes (Agostinho et al. 2007) that use vision as their major source of environmental information ¨ st et al. 2005) and for detecting their prey (Piana (Engstro¨m-O et al. 2006), e.g. midge larvae (Chironomidae) and ostracods (Cyprididae) (Cripa et al. 2009). Therefore, these fishes depend strongly on the relative degree of structural complexity and turbidity to avoid predators and to find food items (Stuart-Smith et al. 2007). With the establishment of submerged macrophytes or an increase in turbidity, visual cues become less reliable as guides for prey consumption (Bro¨nmark and Hansson 2012). Submerged macrophytes provide structural complexity that increases refuge availability for prey (Savino and Stein 1982; Stansfield et al. 1997) and limits the swimming movement of certain predators (Diehl 1988). Turbidity decreases the contrast between the background and the prey (Utne-Palm 1999), providing a visual refuge (Jacobsen et al. 2004; Rana˚ker et al. 2012) by making prey less noticeable and decreasing the predator’s reaction distance (Confer et al. 1978). Thus, both www.publish.csiro.au/journals/mfr
Effect of habitat complexity and turbidity on predation
variables may produce a decrease in feeding activity for visual predators. However, an increase in structural complexity or turbidity may not only reduce prey consumption rates but can change prey selection (e.g. Carter et al. 2010). In structured habitats, visual predators such as Perca fluviatilis and Galaxias auratus do not show a significant reduction in prey consumption, but they do show clear evidence of a change in prey preference (Persson and Eklo¨v 1995; Stuart-Smith et al. 2007). Predators then tend to select the easiest (most vulnerable) prey to capture (Shoup and Wahl 2009). In addition, with increased turbidity, prey reduce (or do not show) antipredator behaviour because of the availability of the visual refuge (Lehtiniemi et al. 2005; Carter et al. 2010). In this way, the true effects of structural complexity and turbidity on predation and prey selectivity may be influenced by prey characteristics, such as mobility (Banks et al. 2000), body size (Do¨rner and Wagner 2003), body colour (Jo¨nsson et al. 2011) and abundance (Allen-Ankins et al. 2012). The balance between prey preference and vulnerability could determine the foraging costs to the predator (Griffiths 1980), and predation rates are not exclusively defined by environmental conditions but, rather, are affected by a combination of prey characteristics and environmental conditions. Lastly, it is possible that the effect of structural complexity on predation rate is influenced by an increase in turbidity (Skov et al. 2002), and this interaction may further determine the lower boundary for prey consumption as well as the selection of a specific prey (Stuart-Smith et al. 2007; Carter et al. 2010). For this reason, understanding the potential effect of the interaction between structural complexity and turbidity on the consumption of different prey types has fundamental importance for addressing issues such as the way in which seasonal variations change the ecological interactions. We tested the following two hypotheses: (1) the negative relationship between structural complexity and predation becomes stronger with the linear increase in turbidity level; and (2) an increase in structural complexity and in turbidity changes prey selectivity from a selective to a random pattern. We considered that our first hypothesis would not be rejected if we found a significant interaction between habitat complexity and turbidity and that our second hypothesis would not be rejected if we observed changes in prey selectivity towards the randomness determined by the increasing structural complexity and turbidity. To test our hypotheses, we conducted a fully crossed designed experiment in which two prey types were available to predators and, for each level of structural complexity, there was a gradient of turbidity. With this experimental design, we simulated different scenarios that are routinely observed in the Parana´ River floodplain. Materials and methods Experimental design The experiment was conducted at the field station of the Centre for Research in Limnology, Ichthyology and Aquaculture – Nupe´lia (Parana´, Brazil, 228450 S, 538150 W). Two prey species (Chironomus sp. and Cypricercus sp.) were simultaneously exposed to predation by a small fish species (Serrapinnus notomelas) in three structural complexity treatments (null
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complexity, NC; low complexity, LC; high complexity, HC) combined with five water turbidity levels (3, 20, 40, 60, 80 NTU) (Fig. 1). These combinations resulted in 15 treatments that were replicated three times for a total of 45 experimental units. White polyethylene rectangular tanks (total volume 60 L; width 30 cm; length 40 cm; height 50 cm; water volume 50 L; depth ,40 cm) were used for the experimental units. Fish were caught in a seine net (length 10 m; mesh 5 mm), and prey were obtained after triaging the material associated with aquatic macrophytes (Eichhornia azurea and E. crassipes) in the Parana´ River floodplain (228460 5500 S, 538200 5900 W). Apical fragments of submerged macrophytes (25 cm) and sediment clay (sun-dried) were also collected at the same location for use in the treatments. All fragments were subjected to a rinsing process to remove any attached material. Predators (standard length 26.6 1.4 mm) were acclimated without feeding for 72 h in a 1000-L tank containing river water. Prey (Chironomus sp., 4.7 1.6 mm; Cypricercus sp., 0.9 0.4 mm) were conditioned in 600-mL bottles (25 individuals per bottle per prey) containing river water. To create the structural complexity treatments, we used various amounts of fragments from the submerged macrophyte Egeria najas. In the NC treatment, no fragments were added, whereas the LC and HC treatments had 6 and 32 fragments added. The numbers of fragments used to attain complexity were chosen based on Figueiredo et al. (2013) and created 3% (sparse and simple bed) and 17% (dense and complex bed) of macrophyte coverage in the experimental units, representing field densities (e.g. Mormul et al. 2012). To simulate macrophyte beds, the fragments were grouped together, tied to a pebble and kept on the bottom of the tank. Each experimental tank received water from the Parana´ River (filtered through 0.02-mm mesh; temperature ,268C), which was used as the first level of turbidity (3 NTU). In a preliminary experiment, we determined the amount of sediment (7 g, 15 g, 21 g and 30 g of clay) needed to create the other turbidity levels (respectively 20, 40, 60 and 80 NTU), a range found naturally in the Parana´ River floodplain (e.g. Rocha et al. 2009). To obtain relative estimates of the distance that visual predators can see through the water column at each turbidity level, we converted turbidity to Secchi disc depth according to Padial and Thomaz (2008). After this conversion, we found that 3, 20, 40, 60 and 80 NTU respectively corresponded to Secchi disc depths of 1.56, 0.63, 0.42, 0.27 and 0.15 m. Before the start of the experiment, we verified that the turbidity would vary by no more than 10% over 4 h, a value that is considered acceptable (Carter et al. 2010). A total of 50 ostracods of the genus Cypricercus (Cyprididae) and nine midge larvae of the genus Chironomus (Chironomidae) were placed in each experimental tank. These invertebrates were then exposed to predation by five S. notomelas. This species belongs to the family Characidae, a group that includes tropical insectivorous fishes (Oliveira et al. 2010) with well developed eyes (Malabarba 1998) that may help in prey detection and capture. We used different initial densities for prey types to simulate the realistic proportions of these types found in the natural environment (e.g. Thomaz et al. 2008). Chironomus and Cypricercus were used as prey because (1) they are common components in the diet of the opportunistic visual predator
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B. R. S. Figueiredo et al.
Habitat structural complexity
Null
Low
High
3 NTU
Turbidity levels
20 NTU
40 NTU
60 NTU
80 NTU
Fig. 1. Schematic drawing of the experimental design emphasising the combination of the structural complexity treatments and turbidity levels.
S. notomelas (e.g. Luiz et al. 1998; Casatti et al. 2003; Piana et al. 2006), (2) they are the most abundant invertebrates associated with the submerged macrophyte Egeria najas in the Parana´ River floodplain (Thomaz et al. 2008), and (3) they display marked differences in their characteristics, such as habitat use, body colour, body size and swimming activity (e.g. Mormul et al. 2006; Higuti et al. 2007). Five predators were added to reduce the effects of individual differences in feeding (Padial et al. 2009) and because we observed schooling behaviour of this predator in the field. The experiment ran for 4 h, during which prey were exposed to predators during the twilight period because predators are more active in this period (Pelicice and Agostinho 2006). At the end of the experiment, the predators were removed, and the macrophytes (if present) were washed to ensure that prey were not attached. The water from each tank was then filtered through a plankton net (65 mm), and the filtrate was stored in plastic bottles and fixed in 70% alcohol to conduct a subsequent prey count. Removing prey by filtration allowed us to recover more than 95% of the prey items (Figueiredo et al. 2013). Data analysis Analysis of covariance (ANCOVA) is a combination of regression and variance analyses that can be used if the response variable, in addition to being affected by a categorical variable, can be affected linearly by a continuous variable (covariable) (Quinn and Keough 2002; Dowdy et al. 2004). We performed an
ANCOVA to test the isolated effect of structural complexity (NC, LC and HC) and turbidity levels (3, 20, 40, 60 and 80 NTU) and the interactive effect between these explanatory variables on the percentage of prey consumed. To comply with the assumptions of normality and homoscedasticity, the response variable was arcsine transformed (Zar 2010). We also conducted an analysis of variance to evaluate the significance of differences in consumption rates among structural complexity treatments and also among turbidity levels for each prey type separately in a protocol similar to Padial et al. (2009). A Tukey post hoc test was applied to perform multiple comparisons among treatments. The initial and final amount of each prey type was used to calculate the Ivlev electivity index (Ivlev 1961). This index was used as a response variable to test the second hypothesis, in which we evaluated whether structural complexity and turbidity produce random prey selection. The values of this index vary between 1.0 and þ1.0, where values between 0 and þ1.0 indicate preference and values between 1.0 and 0 indicate prey rejection (Krebs 1989). We then performed Student’s t-tests for each species and treatment against the constant zero mean to test the randomness of prey consumption determined by the structural complexity and turbidity. This approach was applied because the consumption of one type of prey may be dependent on the other, which prevents comparisons of selectivity between prey types but allows comparisons among treatments. Significant results indicate non-random prey selectivity. All of the tests
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(a) 0.3
Table 1. Significance of analysis of covariance (ANCOVA) for the effect of turbidity, structural complexity, and their interaction, and the significance of t-test of angular coefficient on the prey consumption m.s.
F
1 2 2 39
0.07 0.36 0.02 0.03
1.91 9.56 0.36
t
0.1
0.18 ,0.001 0.70
0
0.95 0.14 1.31
0.35 0.89 0.20
1.4
Prey consumption (% ind.)
0.2
P
⫺0.1
Ivlev index
Turbidity Structural complexity Interaction Error Null complexity Low complexity High complexity
d.f.
173
1.2
⫺0.2 NC
LC
HC
Structural complexity (b) 0.3
1.0 0.8
0.2
0.6 0.1
0.4 0
0.2
⫺0.1
0 0
20
40
60
80
Turbidity (NTU) Fig. 2. Percentage of prey consumed (arcsine transformed) by Serrapinnus notomelas in different turbidity levels and in three structural complexity treatments (open circle and dotted line, null complexity; closed diamond and dashed line, low complexity and, open triangle and full line, high complexity).
were performed using Statistica (Statsoft Inc. 2005), and the results were considered significant if P , 0.05. Results The structural complexity treatments affected prey consumption differently (Table 1). Predators consumed more prey when no complexity treatment was used, whereas the prey consumption was similar for the low- and high-complexity treatments. However, a gradual increase in turbidity did not significantly affect prey consumption, and the interaction between structural complexity and turbidity was not significant (Table 1, Fig. 2). In addition to the differences shown in prey consumption among the structural complexity treatments by the ANCOVA, the ANOVA results showed that prey type appears to have different responses to structural complexity. Prey consumption differed according to prey type and varied with structural complexity. Cypricercus consumption was strongly reduced in LC relative to NC (F2,42 ¼ 20.97, P , 0.001), and the HC treatment did not decrease the consumption of Cypricercus more than did the LC treatment. Conversely, Chironomus consumption did not decrease with LC, and only the HC treatment produced a significant decrease in Chironomus consumption relative to the
⫺0.2
3
20
40
60
80
Turbidity (NTU) Fig. 3. Mean and standard error of the Ivlev electivity index, related to selectivity on Chironomus sp. (closed circle) and Cypricercus sp. (open square) in different structural complexity treatments (NC, null complexity; LC, low complexity; HC, high complexity; trials with different turbidity levels are combined) (a) and at different turbidity levels (in the null structural complexity treatment) (b).
NC treatment (F2,42 ¼ 32.53, P , 0.001). Thus, the consumption rates of both prey species were similar only in the HC treatment. In the NC and LC treatments, we found that there was prey selection by S. notomelas (respectively t ¼ 4.1, P , 0.001 and t ¼ 4.2, P , 0.001) and that Chironomus was consumed more than Cypricercus. In contrast, there was no prey selection in the HC treatment (t ¼ 0.34, P ¼ 0.74) (Fig. 3a). Conversely, predators consumed their prey randomly in all turbidity treatments (all t , 3.44 and all P . 0.07 for Chironomus; all t , 3.19 and all P . 0.09 for Cypricercus) (Fig. 3b). Discussion Our results demonstrate that the interaction between structural complexity and turbidity does not shape predation because the results showed a non-interactive effect on prey consumption, as also reported in other studies (e.g. Gregory and Levings 1996; Figueiredo et al. 2013), suggesting that the influences of structural complexity and turbidity on predation may not covary
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under natural environmental conditions (Snickars et al. 2004). Hence, we rejected the first hypothesis of covariance because the visual refuge provided by suspended particles did not enhance the effect of the physical refuge for the prey. Moreover, the isolated effect of a linear increase in turbidity on predation was not significant, indicating that water transparency could not negatively affect predator feeding. Small changes in the turbidity level (,20 NTU from one experimental level to the next) were not sufficient to significantly reduce the number of prey consumed, suggesting that visual encounters between predators and prey decrease nonlinearly with increasing turbidity (Aksnes and Utne 1997). In contrast, if extreme values are compared (e.g. from 3 to 80 NTU), we observed a tendency for predation to decrease. Several alternative explanations may be suggested. First, prey consumption was not reduced at intermediate levels of turbidity because predators could increase their movement under these conditions (Gradall and Swenson 1982; Gray et al. 2011; Weibel and Peter 2013) and would therefore stabilise the encounter rate between predator and prey (Sweka and Hartman 2001; UtnePalm 2002). In contrast, in extremely turbid aquatic systems, where the reaction distance is minimal, an increase in predator movement is ineffective because it elevates energy expenditure without necessarily increasing success in prey capture (De Robertis et al. 2003; Carter et al. 2010). Second, a gradual and slow increase in turbidity may allow these fishes to adjust the visual detection of their prey in response to changes in environmental conditions (Webster et al. 2007; Helenius et al. 2013). In addition, the decrease in visual detection could be compensated by the increase in detection resulting from non-visual cues from the prey (Lehtiniemi et al. 2005), for example, via the lateral line, mainly in fishes adapted to regular temporal shifts (Webster et al. 2007). In contrast, the significant decrease in prey consumption due to increased structural complexity may be caused by the highquality physical refuge for invertebrates provided by submerged macrophytes (Diehl 1988; Scheinin et al. 2012), which could decrease the encounter rates between predators and prey (Priyadarshana et al. 2001; Turesson and Bro¨nmark 2007). Moreover, with high structural complexity, it is possible that small-sized fishes have limited pursuit movements and could, consequently, show reduced feeding (Heck and Crowder 1991). However, note that, in general, the foraging efficiency of S. notomelas on Chironomus was greater than on Cypricercus although Chironomus was the less abundant prey. These findings may be related to certain characteristics of Chironomidae that facilitate detection, such as lower mobility, large body size and reddish colour (Johansson 1993). On the basis of optimal foraging theory, which suggests that the energetic costs involved in the search, capture and handling of prey must not be greater than the energy benefits provided by the food, we suggest that predators consumed more Chironomus because it offered the greatest amount of energy for the lowest investment of energy although it was the scarcest prey (MacArthur and Pianka 1966). The same pattern of predation on Chironomus has also been found for other fish species in the tropics (e.g. Padial et al. 2009) and for birds (Pedro and Ramos 2009) in temperate regions. The effect of structural complexity on prey consumption differed according to prey type. Cypricercus consumption was
B. R. S. Figueiredo et al.
reduced more than Chironomus in the LC condition, but the consumption of both prey types was similar in HC. This effect could be a result of the relationship between macrophyte cover and prey body size (Grenouillet and Pont 2001). The presence of few interstices among macrophyte fragments in low structural complexity may be sufficient to improve the refuge for Cypricercus, which is smaller in body size than Chironomus. We argue that in highly complex habitats, prey selectivity could be prevented because both prey species can hide amid macrophyte fragments (e.g. Persson 1993), decreasing the encounter rate and decreasing the specificity of the predator diet (Persson and Eklo¨v 1995; Matthews 1998). Thus, food selection based on visual cues may become weaker under conditions of high structural complexity. Therefore, predators could obtain their prey primarily by random encounters and based on non-visual cues. In addition, we found that the turbidity level did not affect prey selection, suggesting that turbidity in natural systems may not alter the relationship between small fishes and their prey choices (Gardner 1981). On the basis of these findings, we partly rejected our second hypothesis because only structural complexity changed prey selectivity from a selective pattern to a random pattern. In summary, we experimentally simulated different scenarios found temporally in floodplains. Despite the limitations that apply if the results of a microcosm experiment are extrapolated to the natural environment, our findings indicate that gradual increases in turbidity may not affect predation and may not alter prey selectivity by a small fish. However, increasing structural complexity can reduce predation, depending on prey type, and can alter prey selectivity by a small fish. Although there are other drivers affecting predation rates in natural aquatic ecosystems, predation could be higher for any prey if these ecosystems lack macrophyte cover. Littoral areas with low macrophyte cover may provide an efficient refuge to smaller prey, such as Cypricercus, whereas an efficient refuge for larger prey, such as Chironomus, could be attained only in littoral areas with high macrophyte cover. Moreover, high macrophyte cover may prevent specificity in prey consumption, causing prey selectivity to follow a random pattern. Lastly, note that our tested scenarios may occur in natural floodplains and that environmental changes, such as great flood pulses or droughts, could alter the pattern of predator–prey interaction shown by our results. Acknowledgements We thank R. P. Tramonte, D. F. Corbetta, G. I. Manetta, L. Fiori and L. A. Lolis for support during the fieldwork and the experimental period. We thank G. C. Depra´ for identifying the fish. B. R. S. Figueiredo is grateful to Coordination of Improvement of Higher Level Personnel (CAPES) for a scholarship. R. P. Mormul and E. Benedito respectively thank the National Council for Scientific and Technological Development (CNPq) for providing the postdoctoral research fellowship and the research productivity grant. Finally, we thank C. C. Bonecker, S. M. Thomaz and R. Fugi for critically reading this manuscript.
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