Neural representation of bat predation risk and evasive flight in moths: A modelling approach
Introduction
Sensory system adaptations for detecting and responding to predator cues are well-known in animals, and the specific neural activity required to generate anti-predator behaviour has been documented for many species (e.g. arctiid moths: Ratcliffe et al., 2009; crickets: Nolen and Hoy 1984; locusts: Fotowat and Gabbiani, 2007; cockroaches: Ritzmann et al., 1980; crayfish: Edwards et al., 1999; fish: Eaton et al., 2001). Most animals co-occur with multiple predator species, each of which provides different cues and poses a different level of threat (Smolka et al., 2011; Falk et al., 2015). Animals often demonstrate anti-predator behaviours that are proportional to the risk posed by the detected predator (e.g. moths: Roeder, 1974, Ratcliffe et al., 2011; crabs: Smolka et al., 2011; frogs: Fraker, 2008; fish: Helfman, 1989; birds: Templeton et al., 2005), but many aspects of predator threat cannot be directly encoded by sensory systems, such as the speed and manoeuvrability of a predator that is stationary at the time it is detected. Behavioural studies suggest that some animals might overcome this limitation by assessing a single detectable trait that is comparable across predators, such as size, to estimate predator threat (Templeton et al., 2005). Few studies, however, have assessed whether sensory systems are specifically tuned to cues that correlate with the predator threat posed by different predators in the community.
Eared moths and echolocating bats are ideal study animals for addressing this question. Echolocating bats are significant nocturnal predators of moths (reviewed in Fullard, 1998). Bats produce ultrasonic echolocation calls for orientation and prey detection, and ultrasound-sensitive ears evolved in many moth families with the primary function of detecting bat echolocation calls (Fullard, 1998; ter Hofstede and Ratcliffe, 2016). With only 1–4 auditory receptor neurons depending on the moth family, these ears are the simplest ears in nature (Yack, 2004). Moths in the family Noctuidae have two auditory receptor cells, called A1 and A2. The A2 cell is approximately 20 dB less sensitive than the A1 cell (Fig. S1). Noctuid moths also have a two-staged anti-bat response; they show directional flight away from quiet ultrasonic pulses, typical of a distant bat, and last-ditch flight manoeuvres in response to loud ultrasonic pulses, typical of a close bat (Roeder, 1962, 1964; Agee, 1969). Directional flight is likely triggered at intensities just above A1 threshold (Roeder, 1964, 1967) and at much greater distances than those at which the bat can detect the moth's echo (Roeder, 1998; Surlykke et al., 1999; Goerlitz et al., 2010), meaning that moths can initiate directional flight to avoid being detected by the bat. Directional flight, however, should no longer be effective once the bat has detected the moth (Corcoran and Conner, 2016) due to differences in flight speeds between bats (4–9 m/s: Table S1) and moths (0.5–6 m/s; Riley et al., 1992; Vickers and Baker, 1997; Luo et al., 2002; Chapman et al., 2008; Corcoran and Conner, 2016). Instead, moths require more drastic erratic flight or other secondary defence strategies (e.g., Ratcliffe and Fullard, 2005, Corcoran and Conner, 2012) to avoid being captured by the attacking bat. Due to the A2 cell's higher threshold, meaning it is only activated by high amplitude sounds, Roeder (1974) hypothesized that A2 cell activity might trigger these last-ditch flight maneuvers. This hypothesis, however, has never been empirically tested in noctuid moths.
The frequency tuning curves of moth A-cells vary depending on the sympatric bat community (reviewed in Fullard, 1998; ter Hofstede et al., 2013), suggesting a strong functional link between frequency tuning of moth ears and the echolocation call frequency of sympatric bats. Although the A1 cell is more sensitive than the A2 cell, both cells have the same shaped tuning curve (Fig. S1), meaning that moths cannot discriminate between different bat species based on frequency (ter Hofstede et al., 2013). Larger moths have lower A1 cell thresholds (i.e. are more sensitive) than smaller moths, presumably to compensate for the greater distances at which bats can detect the louder echo reflected from their larger surface area (Surlykke et al., 1999; ter Hofstede et al., 2013). Despite differences in tuning and sensitivity between moth species, the general shape of the auditory tuning curve is similar across most moth species, with greatest sensitivity to lower ultrasonic frequencies and decreasing sensitivity at higher ultrasonic frequencies (Fig. S1; Fullard, 1998; ter Hofstede et al., 2013; ter Hofstede and Ratcliffe, 2016). The relatively consistent shape of the moth auditory tuning curve across species suggests that it is either constrained by morphological or physiological factors or that it is a functional adaptation, i.e. a matched filter (Wehner, 1987; Römer, 2016; von der Emde and Warrant, 2016).
Here we use both empirical data and mathematical modelling to assess whether the shape of the moth auditory tuning curve is adapted such that moths detect different bat species at times when they pose a similar level of threat. First, we tested for relationships between bat call frequency, which varies across bat species in a community, and four variables that influence the detectability of calls by the moth and echoes by the bat (call level, duration and repetition rate) or the time required for the bat to intercept the moth (bat flight speed). Strong relationships between these variables would allow for the evolution of moth ear frequency tuning that reflects the threat posed by different bat species. Second, we incorporated data on bat echolocation call parameters, bat and moth hearing thresholds and estimated detection distances between bats and moths into multiple models of moth escape behaviour to develop testable hypotheses about the adaptive value of moth ear tuning for evading bat predators at a distance or at close range.
Section snippets
Methods
We compiled data from the literature and our own measurements on echolocation call peak frequency, call duration, apparent call source level, call interval and flight speed for 14 European bat species (Table S1) that hunt flying moths (Barlow, 1997; Vaughan, 1997; Andersson et al., 1998; Bogdanowicz et al., 1999; Dietz et al., 2009). For our own measurements, we used acoustic flight path tracking to obtain these data for five European bat species (Table S1, for methods see Goerlitz et al., 2010
Calculating detection distances between bats and moths
To test whether the frequency tuning of the moth ear might function as a filter to match detection thresholds with the threat posed by different sympatric bat species, we calculated the maximum distances over which bats can detect moth echoes (bat detection distance, or bat-DD) and over which the A1 and A2 cells can be triggered by bat calls (A1-DD and A2-DD, Fig. S2). We calculated these values for all combinations of 14 European bat species (Table S1) and 12 European moth species (Table S2).
The constant buffer hypothesis
Noctuid moths show directional flight away from quiet pulses of ultrasound (Roeder, 1962, 1964; Agee, 1969). The A1 cell of the moth ear is more sensitive than the A2 cell (Fig. S1; Fullard, 1998; ter Hofstede et al., 2013), and directional flight is likely triggered at intensities just above A1 threshold and below A2 threshold (Roeder, 1964, 1967). Previous studies have shown that A1-DD is much greater than bat-DD (Roeder, 1998; Surlykke et al., 1999; Goerlitz et al., 2010), meaning that moths
The matched onset and fixed onset hypotheses
To explain how the shape of the A2 cell tuning curve might be adaptive, we propose two mutually exclusive hypotheses. The matched onset hypothesis (Fig. 5a) postulates that last-ditch flight is initiated by the moth at the same distance at which the moth is detected by the bat (or possibly a constant spatial offset prior to that). In other words, using A2-DD as proxy for the initiation of last-ditch flight, this hypothesis postulates that the distance at which the moth A2 cell responds to bat
Discussion
Our model results support the hypotheses that 1) echolocation call frequency of bats is correlated with variables that correspond with the predation threat they pose, 2) the shape of the A1 cell tuning curve is adapted to allow moths to avoid detection by bats with a similar spatial safety margin across bat species in their community, and 3) bats of the same species are detected by the moth A2 cell at a similar distance, regardless of moth species or size. In addition, the model suggests that
Conclusions
To avoid responding to predators too late or too early, prey animals must correctly estimate the threat of multiple predators. Threat, however, depends on many predator-specific parameters and cannot always be directly perceived or assessed. In contrast, biophysical constraints in predators can result in correlations between particular predator cues that are detectable by prey, such as size or sound frequency, and the level of threat posed by a predator. Prey animals can benefit from sensory
Declaration of Competing Interest
None.
Acknowledgements
Data collection was funded by the BBSRC (BB/f002386/1) to MWH. Model development and analysis was funded by the German Research Foundation (Emmy Noether, GO 2091/2-1 and GO 2091/2-2) to HRG. HtH was supported by funding from Dartmouth College. We are grateful to two anonymous reviewers for their valuable comments on an earlier version of this manuscript. We thank J. Memmott and G. Jones for assistance with collecting moths, Jim and Di McPetrie (Middlegrounds Farm Slapton), Slapton Ley Field
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