Size-dependent functional response of Xenopus laevis feeding on mosquito larvae

Introduction

Predator–prey interactions are important in regulating prey populations and determining the structure of aquatic communities (Brooks & Dodson, 1965; Carpenter, Kitchell & Hodgson, 1985). Predators directly impact prey populations by causing a decline in survival and recruitment, whereas prey quantity and quality directly affect feeding rate, growth, density, reproductive success and population dynamics of predators (Miller et al., 1988; Luecke et al., 1990; Beauchamp, Whal & Johnson, 2007). Consequently, these interactions can affect the distribution, habitat choice, behaviour and foraging strategies of both predators and prey (Eggers, 1978; Sih, 1982; Walls, Kortelainen & Sarvala, 1990). Classical predator–prey models typically assume that individual predators within a population are functionally equivalent (Lotka, 1956; Volterra, 1928; Rosenzweig & MacArthur, 1963). However, most species undergo considerable change in size during their ontogeny. Changing scaling relationships between predators and prey are known to produce nonlinear interactions, with intermediate size predators imposing the strongest per capita top-down interactions (Vucic-Pestic et al., 2010). Size differences of prey may have significant consequences for predator–prey interactions (Jansson et al., 2007; Rudolf, 2008; McCoy et al., 2011). The few studies that have quantified how predator size influences shapes of functional responses on the same prey have highlighted size-dependence of predator handling time and attack rate (e.g.,  Eveleigh & Chant, 1981; Vucic-Pestic et al., 2010; Milonas, Kontodimas & Martinou, 2011; Anderson et al., 2016), and even the general form of the functional response (Anderson et al., 2016). These studies show that assuming that predators of the same species are functionally equivalent may greatly oversimplify our understanding of within-species functional diversity and undermine our ability to predict predator effects on prey.

The functional response is the key relationship linking predator and prey dynamics and describes a predator’s uptake of prey as a function of the prey density. Holling (1965) described the three most common models of predator functional response. A Type I response is characterized as having a constant attack rate a with no handling time h (Holling, 1959; Hassell, 1978). A Type II response incorporates handling time and, as a result, the rate of prey consumption by a predator declines at higher prey densities due to handling constraints. Handling time is the period predators are occupied with processing (e.g., ingesting, digesting) captured prey and are not able to engage new prey items. This constraint can produce nonlinearity to the relationship between prey availability and prey eaten. Predators that exhibit a Type II response typically de-stabilise prey populations due to the positive feedback on prey population growth caused by decreased predator consumption rates as a prey population increases, as predators are unable to regulate prey populations at densities beyond predator satiation (Rosenzweig & MacArthur, 1963; Oaten & Murdoch, 1975). A Type III response is defined by an accelerating increase in prey capture with increasing prey density for a range of low prey densities. The proportion of prey consumed initially increases with increasing prey availability then declines as in a Type II response (Holling, 1959; Hassell, 1978). This can create a refuge for prey at low densities, facilitating the persistence of prey populations, and a physical refuge in limited supply can create a Type III response. Therefore, the type of functional response a predator exhibits can result in quite different outcomes for prey. By describing the response, the potential impact at a population level may be elucidated (e.g.,  Rosenzweig & MacArthur, 1963).

Several factors may influence the type of functional response exhibited for a specific predator–prey interaction. This includes environmental conditions (e.g.,  Laverty et al., 2015; Englund et al., 2011) as well as body size of participants (e.g.,  Brose et al., 2006; McCoy et al., 2011; Tucker & Rogers, 2014; Anderson et al., 2016). Size variation is a common feature in animal populations and influences predator–prey interactions, competition and individual life histories (Ebenman, 1988; Wilbur, 1988; Samhouri, Steele & Forrester, 2009; Asquith & Vonesh, 2012). However, preferred prey typically change with ontogeny for many predators such that experiments are not able to present common prey across a range of predator sizes. For example, Milonas, Kontodimas & Martinou (2011) investigated the functional response of different instars of larval ladybirds (Nephus includens) using increasing prey sizes; all exhibited the same functional response type (Type II), but showed small differences in handling time and attack rate. For iteroparous amphibians with indeterminate growth and overlapping cohorts, individual body size is especially important (Márquez, Esteban & Castanet, 1997; Werner, 1994). Smaller predators in these populations may be limited by the range of prey size they can consume (e.g., handling time may be greater for smaller predators; Anderson et al., 2016) and are often more efficient at assimilating consumed prey due to their high metabolic rates (Werner, 1994; Asquith & Vonesh, 2012). In contrast, their larger conspecifics are generally less efficient in converting prey biomass into predator biomass but may have a much broader range of prey sizes that they can consume (Schoener, 1969; Asquith & Vonesh, 2012; Cohen et al., 1993). In these populations, smaller predators may then have to deal with competition from larger predators that may result in a recruitment bottleneck that could potentially extend the period of time smaller predators remain at a vulnerable size (Schröder et al., 2009; Asquith & Vonesh, 2012). Therefore, understanding the relationship between consumer size and their feeding rates can provide insights into intra-cohort interactions and population dynamics of structured predator populations.

To investigate the role of predator size on functional response, we conducted a comparative functional response study between African clawed frogs, Xenopus laevis, of different sizes on a single prey type mosquito larvae, Culex pipiens, in order to answer the following questions: (1) Do differences exist in functional response type between different sized predators of the same species for a standardised prey size? (2) Are there differences in the functional response parameters (attack rate, handling time, and maximum feeding rate) of different sized predators?

Material and Methods

Results

Functional response model

Logistic regression indicated that of the three size classes of X. laevis, small frogs clearly exhibited a Type II functional response, as revealed by the significantly negative first-order term (Table 1, Fig. 1). The scaling exponent, q, was therefore fixed at 0. Logistic regression indicated Type III responses for medium and large size classes (Table 1, Fig. 1). For these size classes, q was unfixed for initial model fitting and then fixed at the generated maximum likelihood estimate. Bootstrapping was performed on the parameters b and h to provide an error estimate.

Table 1:

Parameter estimates from logistic regression analyses of proportion of prey (C. pipiens) consumed against initial prey density for small, medium and large size classes of X. laevis predators.

Values for 1st order and 2nd order terms are presented with p values.

Size class	Intercept (p-value)	1st order (p-value)	2nd order (p-value)	Functional response type
Small	2.541 (<0.001)	−0.007 (<0.001)	–	II
Medium	−0.106 (<0.05)	0.0045 (<0.01)	−0.000006 (<0.01)	III
Large	−1.494 (<0.001)	0.0098 (<0.001)	−0.000015 (<0.001)	III

DOI: 10.7717/peerj.5813/table-1

Table 2 provides estimates for the functional response parameters b and h for all size classes studied and q in the case of medium and large X. laevis. The only differentiation occurring between functional response curves was at low prey densities (i.e., 0–100) where small frogs had higher predation rates compared to medium and large size classes (Fig. 1). This was supported by the higher attack rate for small size classes (Fig. 2A). Responses converged at higher densities between medium and large size classes as well as small and large size classes, with overlapping confidence intervals for the asymptotes (Fig. 1) and handling time coefficients (Fig. 2B) overlapping. Handling time coefficient was highest in the smallest predator size class, and lowest in the medium size class, with a significant difference (Table 2; Fig. 2B). Handling time for frogs in the largest size class was intermediate, and not significantly different between medium and small frogs (Fig. 2B).

Discussion

We found changes in the basic form of the functional response type between different sized predators of the same species for a standardised prey size. The smallest predator size class exhibited a Type II response compared to Type III responses as exhibited in medium and large adults. This finding has important implications for understanding how predator–prey dynamics change in systems where predators undergo large changes in body size relative to their prey through ontogeny. Moreover, we show predator attack rates and handling times change with predator size. Search efficiency was found to be inversely proportional to predator body size whereas handling time exhibited a U-shaped function and maximum feeding rate was observed in medium sizes of X. laevis. Predators of the same species are often assumed to be functionally equivalent, despite individual variation in predator traits known to be important for shaping predator–prey interactions, like body size (McCoy et al., 2011, but see González-Suárez et al., 2011). This assumption may greatly oversimplify our understanding of within species functional diversity and undermine our ability to predict predator effects on prey. Here we examine the degree to which predator–prey interactions are functionally homogenous across a natural range of predator body size.

Frequently, handling time initially decreases with increasing predator size, which can be attributed to an increased digestive capacity and gape size (Mittelbach, 1981; Persson, 1987). However, Persson et al. (1998) theorised that handling time will decrease until it reaches a minimum value, as found by Mittelbach (1981), and at some point will begin to increase with predator size (e.g., Persson, 1987). This is consistent with our findings where medium sized predators were found to have the lowest handling time, potentially representing the minimum amount of handling time across all size classes. A possible explanation is that large predators will have difficulty in handling very small prey and small predators may have an increased handling time due to their digestive capacity or the prey being large to ingest by inertial suction (Persson, 1987). Therefore, it might be expected that these larger predators will favour larger prey in order to increase their capture success rate. However, there are multiple examples in the literature that show X. laevis predators, independent of size, predominantly consume zoobenthos and zooplankton (Courant et al., 2017). This could be attributed to prey availability and density where the lower limit for prey size consumption depends on prey encounter rate and the cost of consumption (Elton, 1927; Owen-Smith & Mills, 2008). Very little movement is required to feed on both zooplankton and zoobenthos which would reduce energy cost and predation risk. Low densities of small prey offer very little reward to large predators which may explain why both medium and large sized predators did not consume high proportions of prey when prey density was low (Griffiths, 1980).

There are a number of examples that exist showing unimodal (‘dome shaped’) relationships between attack rate and predator size (Aljetlawi, Sparrevik & Leonardsson, 2004; Tripet & Perrin, 1994; Werner, 1988). In aquatic predators, the initial increase of attack rate with predator size is most likely due to an increase in predator search speed, which will positively affect prey encounter rates (Keast & Webb, 1966; Schoener, 1969). The eventual decline in attack rate with increasing predator size could be due to either prey being relatively too small to be detected or the inability of a predator to make fine-tuned movements, resulting in lower prey capture success rate (Hyatt, 1979). However, in our study, attack rate was not dome shaped with respect to prey size and instead negatively correlated with size class (Table 1). One explanation is that the dome shape may only be observed if the experiment had additional intermediate predator size classes. Therefore, attack rate may yet hold a dome shaped function of predator size, which may exist between the small and medium size classes measured in this study. Another explanation for the negative correlation could be that the prey are already at the optimal size for maximum attack rate in small sized predators. There is also a possibility that the relative fitness gain from small prey items is too small to make it worthwhile for larger foragers to be active.

Milonas, Kontodimas & Martinou (2011) found different feeding modes in a predatory ladybird (Nephus includens) in which smaller instars (2nd instar, 2 mm) were found to partially consume prey of different sizes, whereas larger instars (4th instar, 3.3 mm) consumed prey whole. The differences in feeding mode between the small and large predators led to differences in handling time when prey size was increased. Smaller predators were able to maintain a constant handling time, whereas larger predator’s handling time increased with prey size. However, in our study all predators completely consumed prey; thus the mosquito larvae were not too large for the smallest frogs to consume. The lower capture success rate found in medium and large predators was most likely due to their limited ability to hold relatively small prey (CJ Thorpe, pers. obs., 2016), similar to observations made on fish (Persson, 1987). Observation data also showed a response from predators to movement from prey. Regardless of the predator’s positioning in relation to the prey, detection was most likely when prey exhibited sudden movements. X. laevis do not principally use visual or olfactory cues in order to detect aquatic prey, and instead rely on their sensitive lateral line systems (see Elepfandt, 1996).

Despite the potentially profound implications for predator–prey dynamics, few studies directly test whether the basic form of the functional response changes with consumer size. Recently, Anderson et al. (2016) found that the form of the functional response changed with predator size (hatchling to larval ambystomatid salamanders), with smaller predators (adult ambystomatid salamanders) being more limited by handling times than large predators. In other words, smaller predators tended to exhibit a Type II functional response while larger predators exhibited a Type I functional response for the same prey. Type II functional responses as defined by Eq. (1), collapse to a Type I functional response when estimates of the handling time parameter overlap zero. None of the predators exhibited a Type III functional response. In this study, we find that both medium and large sized X. laevis showing a Type III response and small predators exhibiting a Type II, smaller predators may be able to exploit prey at low densities. There is a trend towards higher q values (or scaling exponent) and a more stabilising response (Alexander et al., 2012).

Thus, the medium size class of X. laevis is most likely to destabilise predator–prey dynamics given fast handling times and a reduction in consumption at low densities as indicated by a lower q than the large size class. Small frogs are likely to destabilise prey at low densities, but overall they have a much lower handling time, and therefore a higher feeding rate. When prey density is low, there is an increase in predation from small predators, and when prey density is high, there would be an increase in predation from larger predators (Rindone & Eggleston, 2011). Densities of X. laevis are known to reach very high levels, especially in invasive populations (e.g., Measey, 2001; Lobos & Measey, 2002; Faraone et al., 2008), but also in natural assemblages (De Villiers, De Kock & Measey, 2016). The present study also has a conservation context as the smaller, but functionally similar, congener X. gilli is threatened by competition from X. laevis (see Vogt et al., 2017). Thus, having a population of predators of different sizes at the same time means that there is little relief for multiple prey species, and could lead to prey extirpation (Hassell, 1978). This could be advantageous, if the prey species is a potential disease vector, as in the case of C. pipiens. Prey may experience a similar scenario with fish in aquatic ecosystems due to many fish species consisting of populations with overlapping cohorts (Werner, 1994). However, in populations where differences in predator size are less pronounced, prey may experience only one type of predator response (Milonas, Kontodimas & Martinou, 2011).

Conclusion

Studies often compare functional responses of native and invasive predators and important inferences are made about the potential impacts of these invaders (reviewed by Dick et al., 2013). However, little research focuses on the potential role predator size could play in determining these functional responses. Predators can change their foraging preference as they age and grow and selecting a single size class in functional response experiments to represent an entire population may not be the best representation of populations with overlapping cohorts and large size ranges. It is important to consider whether the same pattern would be seen on different prey species. How would functional response curves be affected if prey size was increased (e.g., see McCoy et al., 2011)? There may be a shift from a Type III to a Type II functional response in our medium and large sized predators as prey size increases. Similarly, it could be asked how prey traits (e.g., activity, shape, colour, etc.) affect functional response curves when size is kept constant. It is therefore important to answer these questions so that a predator population’s functional response is correctly represented. This study has shown parameters such as attack rate, handling time and maximum feeding rate as well as functional response type are dependent on predator body size. Therefore, when conducting a functional response experiment it is vital to consider both predator and prey size, foraging strategy and prey species.

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