<figure><img src="image_70.png" /><figcaption>**FIGURE 2** | GABA-like immunoreactivity in the lateral accessory lobe and surrounding area in the silkmoth. Confocal stacks at the different depth are shown. Anti-GABA antibody was used for the immunoreactivity (wholemount brains; Namiki et al., 2013). D, dorsal; mALT, medial antennal lobe tract; L, lateral.</figcaption></figure>

<figure><img src="image_71.png" /><figcaption>**FIGURE 3** | Serotonin-like immunoreactivity of the lateral accessory lobe and surrounding area in the silkmoth. Confocal stacks for different depth are shown. Anti-serotonin antibody was used for the immunoreactivity (vibratome sections; Iwano et al., 2010).</figcaption></figure>

## Pheromone Orientation

Male moths orient to conspecific females by the use of sex pheromones. The circuit within the LAL generates pheromone-evoked persistent firing in the silkmoth (Kanzaki et al., 1994; Namiki et al., 2014). The identical pheromone input can cease persistent activity. The neuronal activity is termed flip-flop, which is a neural signal named after the toggle property. Extracellular recording studies show (1) the correlation between flip-flop neural signals from descending axons and antennal positions and (2) the correlation between antennal positions and turning direction (Olberg, 1983). From these observations, the flip-flop signal is thought to mediate walking commands for pheromone orientation.

Three types of DNs that show flip-flop neural signals have been identified so far (Mishima and Kanzaki, 1999; Wada and Kanzaki, 2005; **Figure 4**). All DNs innervate the LAL. Group-IIA DN has smooth processes in the ipsilateral LAL and the varicose processes in the contralateral LAL (Mishima and Kanzaki, 1999),

which are the indicators for the postsynaptic and presynaptic terminals (Cardona et al., 2010). Group-IIA and group-IID DNs have smooth processes in the ipsilateral LAL and descend ipsilateral neck connective (Wada and Kanzaki, 2005). The axonal projection in the ventral nervous system is unknown in the silkmoth.

Pheromone information is processed by multiple neural circuits in the silkmoth brain and the pheromone-evoked persistent firing activity is only observed in the neurons innervating the LAL (Namiki et al., 2014), suggesting that the LAL is the site where the flip-flop neural signal is produced.

## Flight-Correlated Activity

Although the behavioral consequence is not known, the sensory response of LAL DNs are examined in the locust under the tethered flight condition (Hornberg, 1994). The flight status is monitored by myographic recordings from the first basaler muscle of the hind wing. The VG3, DN innervating the ipsilateral LAL and descending the contralateral neck connective, shows
<figure><img src="image_72.png" /></figure>

**FIGURE 4** | The morphology of flip-flop descending neurons innervating the lateral accessory lobe in the silkmoth. Frontal and dorsal views of the group-IA DN (**A,B**) and the group-IIA DN (**C,D**) are shown. Group-IA DN has smooth processes mainly within the ipsilateral LAL and varicose processes in the contralateral LAL, ventral protocerebrum, posterior slope, and the gnathal ganglia (**A,B**). Group-IIA DN has smooth processes in the ipsilateral LAL, ventral protocerebrum and posterior slope, and varicose processes in the ipsilateral posterior slope, gnathal ganglia (**C,D**). Outline of the LAL is shown with broken line (blue). The range of maximum intensity projection for (**D**) is shown by broken line in (**C**). GNG, gnathal ganglion; Oe, esophagus; VPC, ventral protocerebrum. Images are prepared based on the data used in Mishima and Kanzaki (1999). A, anterior; LALC, lateral accessory lobe commissure; GNG, gnathal ganglia; Oe, esophagus; P, posterior; VPC, ventral lateral protocerebrum.

wind-elicited excitation, which precedes the onset of flight motor activity. The dendritic branch is concentrated in the ventral shell of the LAL. The other ipsilateral LAL DNs also show the flight-preceding activity and innervation mostly to the ventral region of the LAL. Further ascending neurons projecting to the LAL show tonic excitation during flight, and these receive the input from wing proprioreceptors. Based on single cell recording and staining data, the LAL appears to link ascending and descending pathways.

## Phonotactic Steering

Female crickets orient toward conspecific males by the use of a calling song. Intracellular recording and staining from brain interneurons reveal the information flow from the ascending neurons via local interneurons toward DNs and indicate the relevance of the LAL for the phonotaxis of the cricket *Gryllus bimaculatus* (Zorović and Hedwig, 2011). Ascending interneurons transmit sound information into the brain. A local interneuron that projects from the axonal area of the ascending
interneuron toward the LAL has been reported (Zorović and Hedwig, 2011).

A bilateral LAL DN termed the B-DC1(5) is thought to mediate sensory-motor pathways for phonotaxis (Zorović and Hedwig, 2013). This neuron has smooth, dendrite-like processes in the ipsilateral LAL and the bleby, axon-like processes in the contralateral LAL, and descends on the contralateral side. The morphology is quite similar to the group-IA DN in silkmoths. The depolarization of the B-DC1(5) elicits walking and steering on the contralateral side, and hyperpolarization causes the cessation of walking. The B-DC1(5) response can follow the temporal structure of the male song both in standing and walking conditions, whereas most of the DNs show state-dependent responses, such as gating (Böhm and Schildberger, 1992; Staudacher, 2001). Additionally, unilateral LAL DN termed the B-DI1(1) innervates the ipsilateral LAL and is able to trigger walking, though the effect is less reliable than the B-DC1(5) (Zorović and Hedwig, 2013).

## Obstacle Negotiating Behavior

Harley and Ritzmann (2010) examined the transition behavior for negotiating obstacles in the cockroach *Blaberus discoidalis*. The authors developed an electrolytic lesioning technique that enabled ablation in a small region, and they examined the effect of the lesion on the behavioral task, including climbing over a block, climbing over/tunneling under a shelf, walking up a wall, and walking in U-shaped track (Harley and Ritzmann, 2010). The authors systematically performed electrolytic lesions within the central complex neuropils and the LAL and evaluated the behavioral abnormality. The lesion within the LAL caused a strong phenotype in most, if not all, obstacle negotiation behaviors, supporting the anatomical observation that the LAL is a major output site of the CX. Additionally, the lesion of the LAL on one side exhibited turning abnormalities in both directions, suggesting the possibility that the turning behavior is not caused by the operation of a single LAL, but rather the coordination of the LAL on both sides is required.

## Backwards Walking

Using genetic engineering in *Drosophila melanogaster*, a recent study identified two pairs of neurons for controlling backwards walking, named the moonwalker DN (MDN; Bidaye et al., 2014). When the MDN is activated using thermogenetics, the probability of backwards walking becomes significantly higher, and the silencing of the DNs nearly abolished the movement. These DNs have putative dendritic innervations mainly to the LAL, which is suggested by a synaptic marker. In contrast to the LAL DNs reported in the other insects, MDN innervates the LAL on both sides. The MDN sends projections to leg neuropils in the ventral nervous system on one side. This pattern of innervation is similar to the other ipsilateral LAL DN, aSP3 (Yu et al., 2010), which shows a striking similarity in the morphology in the brain to the group-II DNs of the silkmoth.

## Polarized Light Processing

Locusts are known for its long-distance migration. They use the polarized light as a sky-compass information for navigation.

The neuronal pathway of the polarized light processing has been investigated in detail (Heinze, 2014). Columnar neurons of the CX respond to sky compass signal in locusts (Vitzthum et al., 2002; Heinze and Homberg, 2007), butterflies (Heinze and Reppert, 2011), and beetles (el Jundi et al., 2015). Because the columnar neurons project to the LAL, the area might be relevant for polarized light processing. Polarized sensitive neurons that can be postsynaptic to the CX neurons have been described (Heinze and Homberg, 2009). One of these neurons connects the ipsilateral LAL to the contralateral triangle and show the polarization opponency, suggesting the polarized light processing in the LAL. The LAL-pPC neuron, projects to the posterior protocerebrum, which may correspond to the posterior slope in the silkmoth and the posterior slope/inferior bridge in *Drosophila*. This neuron might supply a polarization-sensitive descending neurons in the locust (Träger and Homberg, 2011).

Overall, these examples suggest the LAL function on locomotion, such as steering toward left or right, and moving forward or backward. Steering-related functions appear to be rare in the other types of DNs, which do not innervate to the LAL, including middle leg contractions for fast escape in the giant fiber (von Reyn et al., 2014), triggering courtship behavior in the pIP10 (von Philipsborn et al., 2011) and the P2b (Kohatsu et al., 2011), grooming in antennal DNs (Hampel et al., 2015), leg motion in a dopaminergic DN in *Drosophila* (Tschida and Bhandawat, 2015), flight initiation in TCG in locusts (Bicker and Pearson, 1983), and song generation in the B-DC-3 of crickets (Hedwig and Heinrich, 1997). In this respect, it would be interesting to identify whether the deviation sensitive DNs, such as the DCI and the PI(2)5 in locusts (Hensler, 1988; Hensler and Rowell, 1990) that are assumed to generate steering responses, have dendritic innervation into the LAL.

## NEURONAL MORPHOLOGY

The LAL is connected to various regions of the protocerebrum (Strausfeld et al., 1998; Namiki et al., 2014). In this section we introduce the morphology of LAL interneurons according to the connectivity. We also examine the comparability in their morphology, which suggests homology of the LAL among insects.

### Central Complex

There are dense connections between the fan-shaped body/protocerebral bridge and the LAL (Shih et al., 2015). Detailed morphology of individual neurons has been reported in *Drosophila*, the desert locust, and the monarch butterfly (Heinze and Homberg, 2008; Heinze et al., 2013; Lin et al., 2013; Wolff et al., 2015). The cytoarchitecture and the morphology of individual neurons seem to be conserved. The LAL receives input from populations of columnar neurons connected to the ellipsoid body via eb-pb-vbo (EIP)/CL1a neurons, protocerebral bridge via the pb-eb-idfp (PEI)/CL1b,d neurons, and the fan-shaped body and protocerebral bridge via the pb-fb-idfp (PFI)/CPU1,2 neurons (Heinze and Homberg, 2008; Heinze et al., 2013; Lin et al., 2013). The basic neuronal components are similar in other
insects, including the cricket (Schildberger, 1983), honeybee (Homberg, 1985), beetle (Wegerhoff et al., 1996), and silkmoth (Namiki et al., 2014) (Supplementary Figure 1). There are connections between the LAL and circuit components of the CX that travel in opposite directions and form functional loops (Lin et al., 2013; Shih et al., 2015).

## Anterior Optic Tubercle

The anterior optic tubercle is the most prominent anterior optic focus in the protocerebrum. There are parallel processing pathways for processing polarized light in the locust. The upper unit of the anterior optic tubercle supplies the LAL, whereas the lower unit supplies the bulb (median olive and lateral triangle; Homberg et al., 2003). Connections between the anterior optic tubercle and the LAL have been reported in *Drosophila* (Yang et al., 2013; e.g., Cha-F-600143, Cha-F-000252, fru-M-800104, Gad1-F-300056), honeybee (Mota et al., 2011), bumblebee (Pfeiffer and Kinoshita, 2012), and silkmoth (Namiki et al., 2014). This pathway underlies sky-compass navigation that uses polarized light in insects (Homberg et al., 2011).

## Superior Medial Protocerebrum

The superior medial protocerebrum is an unstructured neuropil located in the dorsal medial part of the protocerebrum. The brain region is identified as the relay station between the lateral protocereberum and the LAL through a pheromone processing pathway in the silkmoth (Namiki et al., 2014). Dye injection into the LAL labels this region. The neurons projecting from superior medial protocerebrum to the contralateral LAL have been identified (Supplementary Figure 2) (Namiki et al., 2014). A connectome study suggested the presence of this connectivity in *Drosophila*. The superior dorsofrontal protocerebrum, which roughly corresponds to the superior medial protocerebrum, is highly connected with the IDFP (mostly overlap to the LAL; Shih et al., 2015), and interneurons that connect these regions are present (e.g., Cha-F-000105, TH-F-200089, TH-F-200092, VGlut-F-000104; Chiang et al., 2011). The connection between the superior medial protocerebrum and the LAL are also indicated by studies based on clonal units (Ito et al., 2013; Yang et al., 2013). The dorsal-anterior-lateral neurons, which are relevant to memory retention, connect the superior dorsofrontal protocerebrum and IDFP (Chen et al., 2012). The F1 neurons, which are a neuronal population involved in visual pattern memory for contour orientation (Liu et al., 2006; Li et al., 2009), innervate LAL, fan-shaped body and the superior medial protocerebrum. The neurons connecting the LAL and the superior medial protocerebrum have also been reported in the flesh fly (Phillips-Portillo and Strausfeld, 2012) the desert locust (Homberg et al., 2003) and the monarch butterfly (Heinze et al., 2013).

The brain region is characterized by several unique features. The interneurons innervating this region respond to multimodal sensory stimulation and often show spontaneous burst activity in the silkmoth (Supplementary Figure 3). Connectomics studies identify the superior medial protocerebrum as a hub in the whole brain network in *Drosophila* (Ito et al., 2013; Shih et al., 2015).

The entire population of output neurons from the mushroom body lobes has been described in *Drosophila*, and the majority of them supply the superior medial protocerebrum (Aso et al., 2014). These anatomical data suggest the importance of this brain region. Neuronal activity preceding walking is reported in the crayfish (Kagaya and Takahata, 2011). The neuron has the dendritic branch in the area close to the central body, which locates potentially similar to the superior medial protocerebrum in insects.

## Lobula Complex

Namiki et al. (2014) identified a pathway from the lobula complex (Figure 5) directly innervating the LAL. The wide-filed dendritic branches in the lobula plate is reminiscent of the lobula-plate tangential cells in Diptera (Hengstenberg et al., 1982). Optic lobe projection neurons from both the medulla and the lobula complex project to the posterior slope in *Bombyx*. However, only the neurons from the lobula complex have additional processes onto the LAL. The direct inputs from the lobula complex to the LAL are also present in *Drosophila* (e.g., VGlut-F-800153 for ipsilateral, VGlut-F-300218 for contralateral LAL; data available from FlyCircuit Database; Chiang et al., 2011). The leukokinin-immunoreactive neurons connecting the anterior lobe of the lobula and the LAL have been reported in locusts (Homberg et al., 2003).

The direct input from the lobula plate to the LAL might enable integration of olfactory and visual information. Walking activity evoked by sex pheromones is modulated by the presence of the optic flow, especially in the surge phase, in the silkmoth (Pansopha et al., 2014). Silkworms utilize visual information to modify locomotor commands to adapt to perturbations in the sensory-motor feedback gain (Ando et al., 2013). Although there is no experimental evidence, the identified direct pathway to the LAL might underlie this behavior.

## Thoracic Motor Centers

There is similarity in the morphology of DNs innervating LALs among insect species (Figure 6). For example, the characteristic morphological features of group-II DNs in *Bombyx* are: (1) cell bodies belong to the cluster located on the anterior surface beside the anterior optic tubercle, (2) they descend the ipsilateral side of the neck connective, and (3) they have smooth processes in the LAL. The neurons that meet these morphological features have been reported in other species, including the sphinx moth *Manduca sexta* (Kanzaki et al., 1991a), the cricket *Gryllus bimaculatus* (Zorović and Hedwig, 2013), the locust *Schistocerca gregaria* (Homberg, 1994), and the fruit fly *Drosophila melanogaster* (Yu et al., 2010). This observation suggests the homology of neuronal morphology for at least some types of DNs. In this respect, testing the axonal projections into the ventral nervous system would be interesting, but has not been studied thus far. Similarly, some bilateral LAL DNs also share their morphological features: group-IA DNs in moths (Figure 6D), VGlut-F-500726 in *Drosophila* (Figure 6E), VG3 in locusts (Figure 6F), and B-DC1(5) in crickets (Homberg, 1994; Mishima and Kanzaki, 1999; Zorović and Hedwig, 2011).
<figure><img src="image_73.png" /></figure>

Potentially similar neurons are present in ants (Yamagata et al., 2007) and dragonflies (Olberg, 1986).

The bilateral LAL DNs have smooth processes and putative dendritic regions in one side of the LAL in most cases. An exception is the MDN that has dendritic innervations in bilateral LALs (Bidaye et al., 2014). MDN-like cells might be implemented in other insect species that show backwards walking, such as the stick insect (Graham and Epstein, 1985). The silkmoth does not show backwards walking at least in normal condition. Even though intracellular recording on a continuous basis has been performed, which targets the LAL over the past two decades, we have never observed this neuron type in the silkmoth.

## Posterior Slope

Although several lines of evidence indicate function of the CX on behavioral control such as place learning (Ofstad et al., 2011), spatial navigation (Neuser et al., 2008; Seelig and Jayaraman, 2015; el Jundi et al., 2015), locomotor control (Martin et al., 2015), the information flow from the CX and thoracic motor centers is still unclear. Because the CX might have very few or no direct descending outputs (Staudacher, 1998; Okada et al., 2003; Cardona et al., 2009; Hsu and Bhandawat, 2016), some other parts of the brain must be involved in relaying the command information. The LAL is the primary candidate because of its dense connections with the CX and the several examples of DNs that control locomotion (Zorović and Hedwig, 2013; Bidaye

et al., 2014). The number of DNs innervating the LAL, however, is much smaller than that in other parts of the brain, such as the posterior slope, lateral protocerebrum, and gnathal ganglion (Strausfeld et al., 1984; Ito et al., 1998; Okada et al., 2003). In the silkmoth, we estimate that approximately 10 DNs innervate the LAL on each side. One possibility is that such a small number of neurons enable a versatile behavioral repertoire. Another possibility is that another brain region, that is downstream to either the CX and/or the LAL, might relay information to the thoracic motor centers.

We postulate that the posterior slope connects the LAL with the major population of DNs and then transmits the information to the thoracic motor centers. The posterior slope is the inferior part of the posterior brain, where extensive arborizations of descending and ascending neurons are observed (Strausfeld, 1976; Ito et al., 2014). Lobula plate neurons supplies this region, and hence is thought to be involved in the processing of motion cues (Strausfeld and Bassemir, 1985; Paulk et al., 2009; Borst, 2014). The posterior slope contains the largest number of DNs in the cerebral ganglia. In all of the species studied thus far, the posterior-ventral part of the brain is densely labeled by backfilling from the neck connective (Kanzaki et al., 1994; Staudacher, 1998; Okada et al., 2003; Cardona et al., 2009; Hsu and Bhandawat, 2016). Additionally, the posterior slope has connections with the LAL, which might be bidirectional. Namiki et al. (2014) reported the connection with the LAL by injecting the fluorescent dye into the posterior slope. From the neuronal morphology
<figure><img src="image_74.png" /><figcaption>**FIGURE 6** | Comparison of neuronal morphology of the LAL bilateral neurons across species. (A) Ipsilateral LAL DN of the silkmoth, *Bombyx mori* (Mishima and Kanzaki, 1999). (B) Ipsilateral LAL DN of the fruit fly *Drosophila melanogaster* (VGlut-F-200356, FlyCircuit Database; Chiang et al., 2011). The neuron has smooth processes in the ipsilateral LAL, vest, wedge and superior posterior slope and descend the ipsilateral neck connective. (C) Ipsilateral LAL DN of the desert locust, *Schistocerca gregaria* (Homberg, 1994). (D) Bilateral LAL DN of the silkmoth, *Bombyx mori* (Mishima and Kanzaki, 1999). (E) Bilateral LAL DN of the fruit fly *Drosophila melanogaster* (VGlut-F-500726, FlyCircuit Database; Chiang et al., 2011). This neuron has smooth processes in the ipsilateral LAL, epaulete, vest, inferior posterior slope and superior posterior slope and varicose processes in the contralateral vest, inferior posterior slope and superior posterior slope. (F) Bilateral LAL DN of the desert locust, *Schistocerca gregaria* (Homberg, 1994). Scale bars = 100 µm. AL, antennal lobe; CB, central body; DC, deep deutocerebrum; LAL, lateral accessory lobe; M, medial; MB, mushroom body; TC, tritocerebrum.</figcaption></figure>

obtained by intracellular staining, there are candidate neurons for these connections. For example, a subpopulation of the LAL interneurons identified so far in *Bombyx* actually have innervations to the posterior slope (**Figure 7**). About half of the LAL interneurons have varicose processes in the posterior slope (40%, *n* = 20 for bilateral neurons; 55%, *n* = 9 for unilateral neurons; Namiki et al., 2014). A group of unilateral interneurons connects the LAL and the posterior slope (Iwano et al., 2010). Neurons connecting the LAL and the posterior protocerebrum have been reported in the locust (Heinze and Homberg, 2009) and butterfly (Heinze and Reppert, 2012).

Next, we considered the possibility that the DNs themselves transmit information from the LAL to the posterior slope. A

subpopulation of bilateral DNs have smooth processes in the LAL in the ipsilateral side and varicose processes in the contralateral side that might mediate the information flow from the posterior slope to the LAL (**Figure 4**). Group-I DNs, all of which show bilateral innervations, have varicose terminal processes in the contralateral posterior slope, and all of the ipsilateral LAL DNs studied so far have varicose terminals in the ipsilateral posterior slope (Mishima and Kanzaki, 1999; Namiki et al., 2014). The putative homologous neurons of the *Bombyx* group-I DNs in *Drosophila* show similar morphological feature. They also have additional innervations in the posterior slope (e.g., VGlut-F-500726; **Figure 6E**, VGlut-F-000150, and fru-F-100073; FlyCircuit Database; Chiang et al., 2011). These anatomical
A LAL unilateral neuron

frontal view

<figure><img src="image_75.png" /></figure>

B

dorsal view

<figure><img src="image_76.png" /></figure>

C LAL bilateral neuron

frontal view

<figure><img src="image_77.png" /></figure>

D

dorsal view

<figure><img src="image_78.png" /></figure>

**FIGURE 7** | The morphology of interneurons innervating the lateral accessory lobe in the silkmoth. Frontal and dorsal views of unilateral LAL interneurons (A,B) and bilateral LAL interneurons (C,D) are shown. Images are prepared based on the data used in Iwano et al. (2010). LALC, lateral accessory lobe commissure; VPC, ventral protocerebrum.
<figure><img src="image_79.png" /></figure>

connections suggest a large degree of interplay between these two circuits.

## Other Regions

The LAL is also connected with other neuropils in the protocerebrum, such as the ventrolateral protocerebrum in the moth (Pfuhl et al., 2013), locust (Homberg, 1994), and *Drosophila* (e.g., Gad1-F-000101, fru-M-300049; Chiang et al., 2011). This region contains descending output (Milde and Strausfeld, 1990; Okada et al., 2003). Although the number of neurons involved in this connection might be small, this pathway might also underlie the transmission of command from the CX (Strausfeld and Hirth, 2013).

Although the number of neurons is small, connections with the mushroom body are present in *Drosophila* (Ito et al., 1998). The connection between the LAL and the medial lobe of the mushroom body has been described in the blowfly *Calliphora erythrocephala* (Strausfeld, 1976). A neuron connecting the LAL and the mushroom body pedunculus has been reported in the cockroach, which are sensitive to mechanosensory stimuli (Strausfeld and Li, 1999).

## INTRINSIC ORGANIZATION

The LAL appears to have modular organization in the silkmoth (Namiki et al., 2014). We here review the intrinsic organization in

the silkmoth by comparing the neuronal morphology with other insects.

The interneurons of the LAL are classified into two groups: unilateral neurons innervating one side of the LAL and bilateral neurons innervating both sides of the LAL (Figure 7). One prominent feature in circuit organization is the dense connection between both hemispheres, which contains bilateral neurons (Homberg et al., 1987; Homberg and Hildebrand, 1989, 1991; Breidbach, 1990; Müller et al., 1997; Dacks et al., 2006). In the silkmoth, about 60 fibers run through the LAL commissure, a bundle of bilateral neurons connecting the LALs on both sides. This neuronal population is thought to have a crucial role on generating walking command (Kanzaki, 1997). Among these, many neurons show GABA-like immunoreactivity (Iwano et al., 2010). Additionally, there are two pairs of bilateral neurons in the LAL with serotonin-like immunoreactivity, which are present also in other species including Lepidoptera, Coleoptera, and Diptera (Dacks et al., 2006). A population of LAL bilateral neurons has been identified by clustering analysis of FlyCircuit Database (Cluster 31 in supercluster XII; Chiang et al., 2011; Costa et al., 2014). The anatomy of single neuron morphology of the LAL bilateral interneurons has been reported in moths including *Heliothis virescens* (Pfuhl and Berg, 2007), *Agrotis segetum* (Figure 8A; Lei et al., 2001), and *Manduca sexta* (Figure 8B; Kanzaki et al., 1991b), fruit flies (Figure 8C; Hanesch
<figure><img src="image_80.png" /><figcaption>**FIGURE 9** | Schematics of the hypothesized organization of the lateral accessory lobe. Inputs from the protocerebrum converge onto the upper division and the descending output originates the lower division. Neuronal response to the sex pheromone, which trigger the locomotion, is longer for neurons innervating the lower division.</figcaption></figure>

et al., 1989; Chiang et al., 2011), crickets (Zorović and Hedwig, 2011), and locusts (Figure 8D; Müller et al., 1997; Heinze and Homberg, 2009). A similar neuron is present in the malaria mosquito *Anopheles gambiae* (Ignell et al., 2005).

The LAL is classified into two subdivisions that are delineated by the LAL commissure that is the prominent bundle connecting the bilateral LALs: upper division and lower division (Iwano et al., 2010). The LAL bilateral neurons can be classified into two morphological classes based on the degree of neurite innervation into the upper and lower divisions (Supplementary Figure 4). The same morphological feature is observed in *Drosophila* (VGlut-F-800201 for the lateral side, VGlut-F-700549 and VGlut-F-800001 for the medial side of the IDFP) and locust (Homberg, 1994). The interneurons with biased innervations to the lower division of the LAL exhibit activity with a longer duration in response to sex pheromones (Supplementary Figure 5) (Namiki et al., 2014). This suggests the functional difference between the upper and lower divisions.

The inputs from the CX terminate in specific sub-regions within the LAL (Heinze et al., 2013; Lin et al., 2013; Namiki et al., 2014), suggesting the presence of a functional module within the LAL. Most of the input from the CX converges onto the upper division of the LAL in *Bombyx* (Supplementary Figure 6) (Namiki et al., 2014), the ventral LAL in monarch butterfly (Heinze et al., 2013), and the dorsal shell of the LAL in locust (Heinze and Homberg, 2008). The columnar neurons project to the lateral side of the IDFP or LAL in the *Drosophila* (Lin et al., 2013; Wolff et al., 2015). In terms of connectivity, the ventral shell might be homologous to the monarch butterfly's dorsal LAL and the upper division of the LAL in *Bombyx* and *Drosophila*. The lobula complex also supplies biased inputs to the LAL (Supplementary Figure 6) and the same morphological feature is observed in *Drosophila* (Namiki et al., 2014). The projection of a population of dopaminergic PPM3 neurons in *Drosophila* seems to be biased toward the lateral side of the LAL (Nässel and Elekes, 1992; Ueno et al., 2012; Alekseyenko et al., 2013).

The dendritic innervation of LAL DNs is biased to the lower division (Supplementary Figure 7, right). For example, group-IA DN has small dendritic field in the upper division and much more innervation in the lower division, and Group-IID DN shows almost no innervation to the upper division (Supplementary Figure 7, left). Overall, the relative volume of innervations in the lower division is significantly more than those in the upper division (Namiki et al., 2014).

Putative homologous neurons of *Bombyx* group-I DNs in *Drosophila* show similar features (VGlut-F-500726, VGlut-F-000150; FlyCircuit Database; Chiang et al., 2011). Their neurite innervations within the LAL are more toward the medial side of the IDFP. This morphological feature seems to be obvious in other LAL DNs such as the MDN, which controls walking direction (Bidaye et al., 2014).

From these anatomical observations, we propose the modular organization of the LAL is common across insects. The upper division integrates the information from multiple protocerebral regions in addition to the CX, while the lower division produces the premotor signal output via DNs (Figure 9).

## CONCLUSION

We reviewed the neuronal components of the LAL in the silkworm and described the neurons with similar morphology in *Drosophila* and other insects. There are plentiful examples for their potential homology at the level of individual neurons, suggesting the presence of a ground pattern organization. Insects adapt to various environments in different ways, but the same basic design of the nervous system may underlie diverse behavioral repertoire. Expanding the application of a comparative neurobiological approach provides a powerful clue to explore these mechanisms.

## AUTHOR CONTRIBUTIONS

All authors listed, have made substantial, direct, and intellectual contribution to the work, and approved it for publication.

## ACKNOWLEDGMENTS

We are grateful to Chika Iwatsuki, Ryota Fukushima, Satoshi Iwabuchi, Poonsup Pansopha Kono, Masaaki Iwano, Evan Hill for their technical assistance, the reviewers for their comments which improved the manuscript. We are grateful to the FlyCircuit database from the NCHC (National Center for High-performance Computing) and NTHU (National Tsing Hua University). This work is supported by JSPS KAKENHI Grant Number 15H04399 to RK.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fphys.2016.00244
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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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# The neural bases of host plant selection in a Neuroecology framework

Carolina E. Reisenman<fnref n="1" />* and Jeffrey A. Riffell²

Understanding how animals make use of environmental information to guide behavior is a fundamental problem in the field of neuroscience. Similarly, the field of ecology seeks to understand the role of behavior in shaping interactions between organisms at various levels of organization, including population-, community- and even ecosystem-level scales. Together, the newly emerged field of “Neuroecology” seeks to unravel this fundamental question by studying both the function of neurons at many levels of the sensory pathway and the interactions between organisms and their natural environment. The interactions between herbivorous insects and their host plants are ideal examples of Neuroecology given the strong ecological and evolutionary forces and the underlying physiological and behavioral mechanisms that shaped these interactions. In this review we focus on an exemplary herbivorous insect within the Lepidoptera, the giant sphinx moth *Manduca sexta*, as much is known about the natural behaviors related to host plant selection and the involved neurons at several level of the sensory pathway. We also discuss how herbivore-induced plant odorants and secondary metabolites in floral nectar in turn can affect moth behavior, and the underlying neural mechanisms.

**Keywords:** Neuroecology, insect olfaction, oviposition, moths, neurons

## OPEN ACCESS

**Edited by:**
Sylvia Anton,
Institut National de la Recherche
Agronomique, France

**Reviewed by:**
Zainulabeuddin Syed,
University of Notre Dame, USA
Marcus Carl Stensmyr,
Lund University, Sweden

**\*Correspondence:**
Carolina E. Reisenman,
Department of Molecular and Cell
Biology, University of California,
Berkeley, 16 Barker Hall # 3204,
Berkeley, CA 94720-3204, USA
creisenman@berkeley.edu

**Specialty section:**
This article was submitted to
Invertebrate Physiology,
a section of the journal
Frontiers in Physiology

**Received:** 10 June 2015

**Accepted:** 28 July 2015

**Published:** 12 August 2015

**Citation:**
Reisenman CE and Riffell JA (2015)
The neural bases of host plant
selection in a Neuroecology
framework. *Front. Physiol.* 6:229.
doi: 10.3389/fphys.2015.00229

## Herbivory and Host Specialization

Herbivory is a major evolutionary achievement in eukaryotic animals and in particular in insects, with nearly half of all existing species feeding on living plants (Gilbert, 1979). Lepidoptera (moths and butterflies) are the largest lineage of plant-feeding organisms, and their evolution is intimately related to the radiation of angiosperms in the Cretaceous (Grimaldi and Engel, 2005). The other large groups of phytophagous insects are found among the Coleoptera and include weevils, leaf beetles, and the long-horned beetles (Grimaldi and Engel, 2005).

The evolutionary processes that cause the diversification of herbivorous insects are not completely understood but host-plant interactions, and in particular plant chemistry, are thought to be a critical factor (Ehrlich and Raven, 1964; Jaenike, 1990; Whiteman and Jander, 2010). Plant volatiles contribute to sympatric speciation and reproductive isolation involving host plant shifts, such as those observed in races of the larch bud moth *Zyraphera diniana* having different host preferences (Emelianov et al., 2003; Syed et al., 2003), and in the apple maggot *Rhagoletis pomonella* (Linn et al., 2003; Olsson et al., 2006). Changes in host plant preferences can occur very fast, particularly in cases in which few genes participate in mediating host plant selections (Linn et al., 2003; Schoonhoven et al., 2005). However, for divergent plants that have converged on a similar

<footnote n="1">¹Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA, ²Department of Biology, University of Washington, Seattle, WA, USA</footnote>
chemical profile, the similarity can facilitate exploitation by related herbivores—an example of this occurs in checkerspot butterfly larvae *Euphydryas chalcedona*, which are stimulated by host plants that have iridoid glycosylates (Bowers, 1983).

While many herbivorous insects feed in many plant species, most herbivorous are specialists, with larvae feeding and adults ovipositing on a small number of closely related plant species, usually within the same family (Jaenike, 1990). Transitions from a generalist lifestyle to specialization are common, and it has been suggested that they resulted from genetically based trade-offs in offspring performance (Jaenike, 1990). Many morphological and physiological adaptations accompany host plant specialization, including detoxification mechanisms against plant defenses and sensory specializations for the detection of host-derived chemical (olfactory and taste) cues (Schoonhoven et al., 2005). In particular, the importance of the chemosensory system in host plant choice, along with the fact that specialists outnumber generalists, suggests that the evolution of insect-plant interactions is based on changes within the insect nervous system (Olsson et al., 2006), with such changes occurring before host plant shifts (e.g., Dekker et al., 2006; Lavista-Llanos et al., 2014).

In this review we focus on the neural mechanisms underlying host plant selection by moths in a “Neuroecological” context, that is, the function of neurons in an adaptive biologically relevant framework. Almost all of what we review here draws from studies in the exemplary giant sphinx moth *Manduca sexta* (Spinghidae, Lepidoptera), as we know much about both the anatomical and functional organization of its chemosensory system and about the olfactory cues that guide host finding in this crepuscular/nocturnal insect.

## Host Plants Chemical Signals

Olfactory signals play decisive roles in the lives of adult moths, including *M. sexta*. Both sexes of this nocturnally active insect must find flowers on which to feed, males must find females, and gravid females must find the proper host plants on which to lay their eggs. A particularly well characterized olfactory-guided behavior of adult *M. sexta* (as well as of many other moth species) is the oriented flight response of males to the sex-pheromone blend released by conspecific females (Baker, 1990; Willis and Arbas, 1991). Corresponding neurophysiological studies have shown that specialized male-specific neurons at several levels of the olfactory pathway faithfully encode the pheromone signal and control male behavior (Schneiderman et al., 1986; Christensen et al., 1996; Heinbockel et al., 1999, 2004; Lei et al., 2002; Dacks et al., 2007; Riffell et al., 2008a).

How the olfactory system process information about feeding and oviposition resources, in contrast, only recently begun to be understood. Both sexes feed on nectar from flowers, but gravid females require host plants also as oviposition sites (Madden and Chamberlin, 1945; Reisenman et al., 2010; **Figures 1A,B**). Thus, females need specialized receptors and neurons for detecting appropriate host plants for oviposition. Like many other hawkmoths, *M. sexta* adults pollinate large, tubular, night-blooming white or pale flowers (Sparks, 1969,

1973; Alarcón et al., 2008) which produce large quantities of volatile organic compounds (VOCs) (Raguso and Willis, 2002; Raguso et al., 2003). In the Southwestern USA, the sacred daturas or jimsonweeds, *D. wrightii* and *D. discolor*, attract *M. sexta* as pollinators and also function as host plants for their larvae (Mira and Bernays, 2002; **Figures 1A,B**). Their flowers provide rich nectar sources to foraging adults (Raguso and Willis, 2002) and bloom once over a single night, but a plant can produce >200 flowers in one season. Floral odors and visual signals, alone or in combination, are innately attractive to adults of both sexes (Raguso and Willis, 2002; Goyret, 2010; Kaczorowski et al., 2012; Riffell and Alarcón, 2013), but only the simultaneous presence of both signals elicits nectar feeding (Ramaswamy, 1988; Raguso et al., 2003).

The selection of appropriate host plants for oviposition by gravid moths is undoubtedly crucial for the success of her larval offspring. In contrast to feeding, female *M. sexta* rely primarily on olfactory cues to locate and identify host plants for oviposition (Sparks, 1973; Ramaswamy, 1988; Reisenman et al., 2010; Späthe et al., 2013). As other Lepidoptera (Renwick and Chew, 1994), female *M. sexta* may contact host plants with their tarsi prior to oviposition (Yamamoto et al., 1969; Sparks, 1973), but this behavior is not a requirement (Mechaber et al., 2002). In general, the contribution of taste to host plant selection is not completely understood, although highly specialized taste receptors are present in specialist herbivores. Once the host plant is located, taste receptors mediate a sequence of behavioral events leading to egg lying (Schoonhoven et al., 2005).

As many herbivores, *M. sexta* moths are highly specialized. Larvae feed almost exclusively (but see Mira and Bernays, 2002; Mechaber et al., 2002) on plants of the family Solanaceae [e.g., native jimsonweeds (*Datura* sp.), native and cultivated species of tobacco (*Nicotiana* sp.), tomato, eggplant, pepper, etc., (Madden and Chamberlin, 1945; Yamamoto and Fraenkel, 1960; Tichenor and Seigler, 1980; el Campo et al., 2001)]. Although both nectar and leaves of some of these plants contain alkaloids which are toxic for many other insect species, moths have detoxification mechanisms that allow them to deal with these secondary compounds (Glendinning, 2002; Adler et al., 2006; Hare and Walling, 2006). Though plant defensive compounds can be toxic to specialist herbivores at high (unnatural) concentrations, on average, specialist herbivores are less negatively impacted than generalists (Berenbaum et al., 1989; Cornell and Hawkins, 2003). Thus, both chemosensory specialization and tolerance to toxic components are both crucial components for insect host plant specialization and coevolution.

## Herbivory and Host Plants: a Dynamically Changing Olfactory Environment

While specific olfactory cues from host plants are necessary for acceptance and egg laying by gravid females (Yamamoto and Fraenkel, 1960), the VOCs released by plants are not static, but are rather affected by the time of the day and other physiological and environmental factors (De Moraes et al., 2001). For instance, plants respond to herbivory with changes in plant chemistry and physiology that make them more resistant to further attack, such as the induction of toxic metabolites that
<figure><img src="image_81.png" /><figcaption>B</figcaption></figure>

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<figure><img src="image_83.png" /></figure>

**FIGURE 1** | (A) Adult *M. sexta* feeding on nectar from a flower from one of its favorite host plants, the jimsonweed *Datura wrightii*. (B) Females also use this plant as an oviposition resource (Picture by C. Hedgcock). (C) Experimental set-up used to study the oviposition behavior of females in the laboratory. Singly mated female are released in a flight cage in a dual-choice experiment. In this case, the insect is offered two plants, one with an artificial paper flower loaded with a synthetic odor (experimental) and the other loaded with the solvent (control).

can poison attacking herbivores or slow their growth (Karban and Baldwin, 1997; Baldwin and Preston, 1999). Plants also use indirect defenses, i.e., synthesize and release complex blends of VOCs that attract the natural enemies of the herbivores (De Moraes et al., 1998; Turlings et al., 1998; Baldwin and Preston, 1999; Paré and Tumlinson, 1999; Dicke and van Loop, 2000; Halitschke et al., 2000; Schnee et al., 2006). These VOCs, which

include monoterpenes, sesquiterpenes, and aromatics (Paré and Tumlinson, 1999) are produced *de novo* (Paré and Tumlinson, 1997), systemically (De Moraes et al., 1998) and slowly (>24-h post-attack; Kessler and Baldwin, 2001), and are qualitatively different from the VOCs released by mechanically damaged plants (Paré and Tumlinson, 1999; Halitschke et al., 2000; De Moraes et al., 2001; Kessler and Baldwin, 2001; Reisenman et al., 2013). For instance, feeding by *M. sexta* larvae on *Nicotiana* sp. (tobacco) induces both direct and indirect defenses (Halitschke et al., 2000; De Moraes et al., 2001; Adler et al., 2006; McCall and Karban, 2006). In principle, a gravid female should avoid ovipositing in such induced plants, as they are likely to host insects that will compete with her offspring and also natural enemies attracted by the induced VOCs. Indeed, both *M. sexta* and *M. quinquemaculata* moths avoid ovipositing on larva-damaged plants (Kessler and Baldwin, 2001) in a plant-species specific manner (Reisenman et al., 2013; Späthe et al., 2013). Alternatively, as observed in other insect species, egg-lying by multiple females in nearby sites could reduce predation risk for each female's offspring (Jaenike, 1978). Also, in certain moth species, oviposition is deterred by VOCs emitted by larval frass (Anderson et al., 1992; Xu et al., 2006) and in *M. sexta* is affected by the presence of con-specific larvae (Reisenman et al., 2013).

Insect herbivores are agents of selection on plant defense traits because plant populations excluded from herbivory evolve lower resistance and higher competitive ability, but these populations can quickly regain increased resistance when re-exposed to the herbivore (Agrawal et al., 2012; Uesugi and Kessler, 2013; Sakata et al., 2014). Thus, moths will be experiencing a spatiotemporally changing landscape of suitable host plants, many of whom will vary in their induced chemical defenses, resources, and growth potential. For instance, induction of plant defense pathways in tomato or *Arabidopsis* sp. results in significant reduction in growth, physiological performance, and fitness (Cipollini, 2010; Corrado et al., 2011), all of which can indirectly affect the growth of the developing larvae. Moreover, this spatiotemporal complexity in the plant community—*via* induction of plant defenses—provide different kinds of information to the herbivore and the plant community: (1) for plants, it can reduce the probability of secondary attacks and provide host cues for the natural enemies of the herbivores, while providing information to and from neighboring plants; and (2) for the herbivore, it provides information about the suitability of the host plant with regards to its chemical defenses and its metabolic and physiological health.

The detection and decision-making ability of adult moths in response to the dynamic host plant chemical information is additionally affected when the host plants serve also as floral nectar resources. For instance, leaf herbivory can result in smaller flowers and fewer open flowers (Mothershead and Marquis, 2000; Adler et al., 2001), leading to lower amounts of floral VOC emissions and less pollinator visitation. Additionally, the biosynthetic pathways of inducible plant defenses can be constitutively expressed throughout the plant tissue. Thus, when damaged, there lies the potential that floral scent is modified (Heil and Ton, 2010). However, results from this hypothesis are mixed: in one study larvae damaged by *M. sexta* of sweet tobacco
(Nicotiana suaveolens) did not significantly increase floral volatile production (Effmert et al., 2008), while in wild tomato plants (S. peruvianum) damaged by the same herbivore the floral blend significantly differed from that of non-damaged plants (Kessler and Halitschke, 2009). For other plant families, induction of plant defenses appears to be systemic and flowers can either produce VOCs de novo in response to herbivory (Loughrin et al., 1994; Röse and Tumlinson, 2004) or decrease emissions altogether (Pareja et al., 2012). Thus, the interplay between pollinator attraction and host plant defense provides a unique system to identify the cues and associated sensory mechanisms by which plants manipulate their interaction with insects.

## A Naturalistic Insect-plant Interaction

In contrast with older studies which use artificially selected crops grown in simple agro-ecosystems (Harvey et al., 2015), much work in the last decade focused on more naturalistic insect-plant interactions. For instance, a particularly interesting mutually beneficial association exists in the Sonoran Desert in Southwest USA between *M. sexta* and the jimsonweed *D. wrightii*: while flowers are pollinated by adults (Alarcón et al., 2008; Riffell et al., 2008b), the plants serve as food resources for the larvae (Mechaber and Hildebrand, 2000; **Figures 1A,B**). Detoxification mechanisms (Glendinning, 2002) enable larvae to cope with herbivory-induced toxic secondary compounds (Adler et al., 2006; Hare and Walling, 2006), and plants cope with the negative effects of herbivory by quickly recovering after larval damage (Reisenman et al., 2013). Importantly, plants benefit from this association because jimsonweeds set more fruit by cross-pollination (Nunez-Farfan et al., 1996; Raguso et al., 2003) and plants are not completely defoliated, as eggs and larvae suffer high levels of parasitism in the field (Strauss and Agrawal, 1999; Kester et al., 2002; Mira and Bernays, 2002). Furthermore, feeding of larva in vegetative tissues, while causing changes in the vegetative VOC profile, does not affect the quantitative and qualitative composition of the floral VOCs that are necessary to mediate feeding attraction (Riffell et al., 2009b; Reisenman et al., 2013). Thus, this insect-plant interaction has been described as a non-obligatory mutualistic pollinator-herbivore association (Bronstein et al., 2009). In the next section we describe the neural pathway/s and substrates used by the moths to detect and locate suitable host plants. We argue that this exemplar insect-plant interaction illustrates an undeniable perspective: that neurobiological experimentation in a “Neuroecology” context has the most chances of helping discovering how neural circuits function to ultimately produce behavior.

## The Moth Olfactory Pathway

The anatomy and physiology of the olfactory system is remarkably similar across insects, including moths. Here we describe that of our exemplar herbivorous insect, the moth *M. sexta*. Antennae are the main olfactory organs of moths: the flagellum of each antenna carries thousands of cuticular hair- or peg-like olfactory organules (sensilla), each of which contains one or a few olfactory sensory neurons (OSNs) (Lee and Strausfeld, 1990). The axons of OSNs in each antennae project centrally via

the antennal nerve and terminate in one of the paired primary olfactory centers in the insect brain (Tolbert and Hildebrand, 1981), the ipsilateral antennal lobe (AL) (Figure 2A). As in all insects, the ALs have numerous glomeruli, the functional modules of the AL and the first synaptic sites in the olfactory pathway (Boeckh and Tolbert, 1993; Sun et al., 1997; Figure 2B). In the fruitfly *Drosophila melanogaster* and likely in all insects, most types of OSNs expresses only one type of olfactory receptor protein (OR), and the axons of OSNs expressing the same OR converge on the same glomerulus (Gao et al., 2000; Vosshall et al., 2000). Males have OSNs which respond to the individual components of the con-specific female sex pheromone (Kaissling et al., 1989), but in some moth species certain plant odorants chemically unrelated to the sex pheromone can activate the male-detecting sex pheromone pathway at both the peripheral and the central level (Rouyar et al., 2015). The antennae of *M. sexta* also have OSNs which respond to volatiles emitted by host plant foliage, including aliphatic, aromatic, and terpenoid compounds bearing a variety of functional groups (Shields and Hildebrand, 2001; Späthe et al., 2013; Figure 7). The labial palps of moths of both sexes also have OSNs that respond to changes in ambient CO₂ (including floral CO₂), a cue that is important in moth behavior, and converge in a single glomerulus in each AL (Guerenstein et al., 2004; Thom et al., 2004; Goyret et al., 2008).

Initial three-dimensional reconstructions based on anatomical analysis indicated that the ALs of *M. sexta* have 63 ± 1 identifiable glomeruli (Rospars and Hildebrand, 1992, 2000) (Figure 2B), some of which were characterized physiologically (Christensen and Hildebrand, 1987; Roche King et al., 2000; Guerenstein et al., 2004; Reisenman et al., 2004, 2005). More recent studies conducted using a non-histochemical approach based on confocal laser scanning microscopy followed by computer-assisted 3D reconstruction indicate that there are actually 70 ± 1 glomeruli in females and 68 in males (Grosse-Wilde et al., 2011). As in other moths (e.g., Berg et al., 2002), the majority of glomeruli (ca. 60) are sexually isomorphic (Figure 2B) and are involved in the processing of olfactory information about plant VOCs and perhaps other odors (e.g., Figure 4C; Guerenstein et al., 2004; Lei et al., 2004; Reisenman et al., 2005; Hillier and Vickers, 2007; Riffell et al., 2009a,b; Varela et al., 2011). A second AL subsystem comprises three male-specific glomeruli (the so-called macroglomerular complex) which process information about the conspecific female's sex pheromone (Figure 2B; Christensen and Hildebrand, 1987; Heinbockel et al., 1999, 2004). Females have a pair of large female-specific glomeruli (LFGs, Figures 2B,C, 4A,B) which respond to particular host plant VOCs (Roche King et al., 2000; Reisenman et al., 2004) and are involved in mediating oviposition behavior (Kalberer et al., 2010), and three smaller female-specific glomeruli (Grosse-Wilde et al., 2011). Correspondingly, three male-specific and five female-specific OR genes have been described in *M. sexta* (Grosse-Wilde et al., 2011). Moreover, sequence data indicates that homologous female-specific OR genes exist in different moth families (Grosse-Wilde et al., 2011), indicating that certain VOCs are important for mediating oviposition behavior across distant species.
<figure><img src="image_84.png" /></figure>

As in many other insects, two classes of AL neurons have been identified in *M. sexta*: local interneurons (LN; $n \approx 360$) and projection neurons (PN; $n \approx 800$) (Figures 2D–F, 4). Many studies indicate that the architecture and function of AL neurons is remarkably similar among moths (e.g., Hartlieb et al., 1997; Lei and Hansson, 1999; Kanzaki et al., 2003; Seki and Kanzaki, 2008; Namiki and Kanzaki, 2011). Most PNs have

dendritic arborizations restricted to a single glomerulus and an axon projecting to higher brain centers (Homberg et al., 1988). Some PNs arborize in multiple glomeruli (Figure 2F), and it is likely that they process information about particular odor blends (Heinbockel et al., 1999). The LNs receive input from OSNs, have dendritic arborizations restricted to the AL, interconnect few or many glomeruli (Figures 2D,E; Matsumoto and Hildebrand,
1981; Reisenman et al., 2011), and interact synaptically (mainly through inhibition, but see Olsen et al., 2007) with other AL neurons (Christensen et al., 1993; Reisenman et al., 2008). The major targets of PN axons are the lateral horn of the protocerebrum (PC), the inferior lateral PC, and the calyces of the ipsilateral mushroom body (Figures 3A-C, 4A; Homberg et al., 1988, 1989). Neurons in these higher-order brain centers integrate information about different odor compounds (Kanzaki et al., 1991; Lei et al., 2013; an example is shown in Figure 3C) and are involved in learning and memory (Davis, 2004; Fahrbach, 2006). Although better characterized in males, downstream neurons in the lateral accessory lobe and ventral protocerebrum (an example is shown in Figure 3D), which are thought to be main target of olfactory-responding protocebral neurons, mediate the moth characteristic olfactory-evoked sequential zigzag turns (Kanzaki and Shibuya, 1992).

Centrifugal neurons perform several important functions in the moth brain by linking different neural networks and modulating neural circuits that together lead to important physiological and behavioral responses. In particular, a small number of large aminergic centrifugal neurons have important behavioral effects. For instance, fibers from octopamine-immunoreactive neurons are found in the AL, mushroom bodies, and the lateral protocerebrum (Dacks et al., 2005); similarly, fibers from dopaminergic and serotoninergic neurons are also found in the AL and other higher brain areas, including the lateral horn. These neuromodulators increase odor-evoked responses in the majority of antennal lobe PNs and LNs, but can also decrease responses in a smaller subset (Dacks et al., 2008, 2012). Thus these neuromodulators can serve to increase the gain and sensitivity of the neural ensemble in the AL—an important feature for the moths when flower and host plants are temporally and spatially dynamic.

## Butterflies and Moths: More Similar than Different

Among herbivorous insects, searching for a suitable host plant may involve input from different sensory modalities (Schoonhoven et al., 2005). However, the importance of olfactory cues in host finding maybe a more generalized phenomenon among the Lepidoptera than previously thought. It has long been assumed that butterflies, which are adapted to a diurnal lifestyle, use mostly visual cues to find host plants. Recent studies in the comma butterfly *Polygonia c-album*, however, showed that the anatomical and physiological characteristics of their olfactory system are remarkably similar to that of moths, despite more than 100 million years of divergence (Carlsson et al., 2011). For instance, the numerical glomeruli composition of AL of this butterfly species is comparable to that of moths, AL neurons faithfully respond to host plant extracts and plant-derived compounds, and odor-evoked AL responses match well described features such as unique and overlapping patterns of activated glomeruli (Carlsson et al., 2011). Also, studies in the butterfly *Pieris rapae*, which has an extremely well developed

visual system, showed that insects can distinguish a host from a non-host plant based solely on olfactory cues (Ikeura et al., 2010).

Another interesting study compared the neural representation of plant-derived odorants in five moth species belonging to two phylogenetically distant families (Sphingidae and Noctuidae). While moths in these two families shared some (but not all) foraging and oviposition characteristics, the basic AL mapping of host plant odorants was comparable across species. Thus, these results demonstrate that similar coding strategies are used even by families separated more than 65 million years ago (Bisch-Knaden et al., 2012).

## Putting It All Together: Plant Chemical Signals, Neurons, and Behavior

Using *M. sexta* as an exemplary, in this section we present our current knowledge on the neural processing of relevant, naturally occurring host plant signals at several levels of the olfactory pathway, and its consequences for natural behavior. As we mentioned before, the sacred *D. wrightii* and the nocturnal moth *M. sexta* form a pollinator-plant and herbivore-plant association (Bronstein et al., 2009), with females using the plant both as a nectar (Alarcón et al., 2008; Riffell et al., 2008b) and as an oviposition resource (Mechaber and Hildebrand, 2000). Correspondingly, feeding and oviposition behaviors often co-occur in gravid females (Bronstein et al., 2009; Reisenman et al., 2010). What are the floral and vegetative VOCs that guide these behaviors, and how are they processed in the moth brain? Although *D. wrightii* flowers produce a bouquet composed of more than 60 odorants (Raguso et al., 2003) a blend of just three floral components [±]-linalool, benzaldehyde, and benzyl alcohol], presented in appropriates ratios and concentrations, is an effective mimic of the floral scent, eliciting feeding behavior in naïve moths of both sexes (Riffell et al., 2009b). Although adult moths are innately attracted to the *D. wrightii* floral scent, they readily learn to feed on other nectar sources through olfactory conditioning (Riffell et al., 2008b).

While OSNs in the female antenna of *M. sexta*, as in other moths (e.g., Hillier et al., 2006; Ulland et al., 2008), respond to a chemical variety of host plant VOCs (Figure 7; Shields and Hildebrand, 2001; Späthe et al., 2013), we found that (±)-linalool, a floral volatile characteristic of many moth-pollinated night-blooming flowers including *D. wrightii*, has important roles mediating behavior (Riffell et al., 2008b, 2009a; Reisenman et al., 2010, 2013). Behavioral and electrophysiological recordings from AL-PNs showed that the two naturally occurring enantiomers of linalool present in flowers mediate feeding and oviposition through two neural pathways, one that is sexually isomorphic and non-enantioselective, and another that is female-specific and enantioselective (Figures 4B, 5; Reisenman et al., 2004, 2010). In one hand, the (+) and (−) enantiomers of linalool respectively contribute to oviposition attraction and repellence and are discriminated by female-specific PNs (Figures 1C, 4, 5). Linalool-responsive sexually isomorphic PNs do not discriminate between linalool enantiomers (Reisenman et al., 2004) and
<figure><img src="image_85.png" /></figure>

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<figure><img src="image_88.png" /><figcaption>**FIGURE 3** | Anatomy and morphology of olfactory neurons arborizing in second and third-order brain centers in *M. sexta*.</figcaption></figure>

These neurons might respond to other sensory modalities. (A) A protocerebral neuron with dendritic arborizations in the inferior lateral protocerebrum (ILP) and the calyx of the mushroom bodies (CMB). (B) In the same preparation a male-specific (MGC) neuron was differentially

stained (in magenta) showing that both neurons have arborizations in overlapping areas (yellow). Scale bar: 100µm. D, dorsal; L, lateral. (C) An olfactory-responsive neuron with arborizations in the CMB. (D) A neuron in the lateral accessory lobe (LAL), an area which receives input from olfactory protocerebral neurons. Scale: 200µm. Same orientation in all panels; D, dorsal; L, lateral.

correspondingly, the enantiomers are not discriminated in the feeding context (Reisenman et al., 2010). Interestingly, two homologous receptors to the *Bombyx mori* linalool-ORs, MsexOR-5 and 6, have been described in *M. sexta* (Grosse-Wilde et al., 2010, 2011), and are likely candidates to mediate (at least in part) these behaviors. This, together with the fact that these moth species belong to evolutionary distant families, suggest that these receptors and the corresponding neurons play an important role in moth, and probably Lepidoptera, olfaction (Grosse-Wilde et al., 2011).

While *M. sexta* uses a variety of host plants for oviposition, choice experiments showed that females prefer to oviposit on *D. wrightti* plants, and that this preference is mostly mediated by olfactory cues (Späthe et al., 2013). Although females avoid ovipositing in larva-damaged plants (Figure 5E), this avoidance is plant- specific: females strongly avoid larva-damaged tomato and tobacco plants, but they do not avoid ovipositing in larva-damaged *D. wrightti* plants, despite that these plants can be

clearly distinguished from non-damaged plants by their VOC profile and by the peripheral OSNs (Figures 6, 7; Reisenman et al., 2013; Späthe et al., 2013). An important consideration is that moths use these plant species differently: while the annuals tomato and tobacco are only used by moths for oviposition, the jimsonweeds are also pollinated by the adults. Thus, we propose that the differences in oviposition preference toward larvae-damaged plants of different species are due to the different relationships between *M. sexta* and these host plants. The beneficial association is emphasized further by the finding that at least some of the *D. wrightii* floral VOCs that are important to mediate feeding- and hence pollination- remained unchanged in herbivore-induced plants (Reisenman et al., 2013).

As we mentioned before, (+)-linalool has an important role in mediating oviposition attraction. In contrast, we found that plants with (−)-linalool added are avoided by ovipositing females (Figure 5D; Reisenman et al., 2010),
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(+)-linalool 10⁻³

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(-)-linalool 10⁻³

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cis-3-hexenyl-acetate 10⁻⁴

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(+/-)-linalool 10⁻²

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control

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**FIGURE 4** | Morphology and odor responses of female-specific antennal lobe PNs (A: medLFG-PN, B: latLFG-PN) and of a sexually isomorphic PN in the adjacent glomerulus G35 (C). Scale bars: 200 m. D, dorsal; L, lateral. The second panel in (A) shows the projection sites of LFG-PNs in the protocerebrum, the lateral horn (LH) and the calyx of the mushroom body (CMB); the optic lobe (OL) and the suboesophageal ganglion (SOG) are shown for reference. Most uniglomerular PNs also project to these sites. Shown are intracellular recordings obtained from these neurons to stimulation (duration = 200 ms,

bars below records) with vegetative material (A) or odors (B,C) at the concentrations (% vol/vol) indicated. In (A) the bottom traces (in gray) show the simultaneously recorded electroantennograms (EAGs). Control stimuli are air from an empty cartridge (A) or the mineral-oil solvent (B,C). Calibration bars: 10 mV (intracellular trace), 1 mV (EAG). Note that medLFG-PNs are excited by tomato leaf volatiles, latLFG-PNs respond differentially to the two linalool enantiomers, and G35-PNs are excited by cis-3-hexenyl acetate and hyperpolarized by (±)-linalool (arrow), i.e., by input to the adjacent latLFG.

and that this compound is significantly increased in larva-damaged tomato plants (Reisenman et al., 2013). The antenna of the cabbage moth *Mamestra brassicae* is, as that of *M. sexta* (Reisenman, not shown), more sensitive to (-)-linalool than to (+)-linalool (Ulland et al., 2006). Collectively, these results suggest that (-)-linalool (alone or together with other with other induced VOCs; Reisenman et al., 2013), might act as an oviposition repellent and also as a plant defense, attracting the natural enemies of herbivores (Baldwin et al., 2002). The finding that this unique odorant is similarly processed and discriminated by moths in different families also suggests that common components and neural mechanisms are involved in the selection of suitable host plants.

Although linalool has important roles in mediating oviposition, it is very likely that the choice of suitable host plant sites is mediated by a suite of VOCs. A powerful technique to address this issue, which has been already used to investigate the VOCs involved in mediating feeding (Riffell et al., 2009a,b), is to couple the use of gas chromatography for chemical detection and multi-unit recordings from AL neurons (GC-MR; **Figure 8A**). This technique allows to simultaneously visualize the activity of many neurons in response to components from behaviorally active plant extracts as they elute from the GC column. For instance, **Figure 8B** shows an example of a neuron that responds specifically to a single larva-damaged component. The use of the multi-unit recording technique allows stimulating many neurons with different bioactive plant
<figure><img src="image_101.png" /></figure>

**FIGURE 5 | Oviposition behavior of *M. sexta* in the laboratory.** In all cases single mated females were offered a choice between a control and a test plant in a flight tent (as shown in **Figure 1C**) and allowed to oviposit during 10 min after take-off. Plant pairs of the jimsonweed *D. wrightii* (**A–D**) or tomato (*Solanum lycopersicum*) (**E**) were used. In (**A**) two control plants were offered to control for spatial asymmetries (n = 25). The following experimental series were conducted: (**B**) a plant with a newly opened flower vs. a plant with a paper flower (n = 12); (**C**,**D**) a plant with a paper flower

loaded with (+)-linalool (C), n = 16) or (-)-linalool (D), n = 16) vs. a plant with a paper flower loaded with solvent (linalool was loaded in the paper flowers at the naturally-occurring concentrations); (E) a larva-damaged plant vs. an intact plant (n = 38). Data represent the percentage (average ± SE) of eggs oviposited in each plant. Moths and plant pairs were used only once. Asterisks indicate significant differences (p < 0.05; Sign tests). Green-hue and red-hue colors, respectively indicate oviposition attraction or repellence for the experimental plant (Data modified from Reisenman et al., 2010, 2013).

<figure><img src="image_102.png" /></figure>

**FIGURE 6 | Plant VOC response to herbivory.** *M. sexta* larvae were allowed to feed on plants during 2–3 days, after which larvae and frass were removed and vegetative VOCs collected and analyzed via GC-MS as described in Reisenman et al. (2013). Representative ion chromatograms of

the headspace from undamaged *D. wrightii* (A), larva-damaged *D. wrightii* (B), undamaged tomato (C), and larva-damaged tomato (D). In (C,D) same numbers or letters, respectively indicate common and different compounds emitted by larva-damaged vegetation.

extracts (Figure 8C). Quantification of individual neuron responses to repetitive stimulation evinced plant- and status-(intact or damaged) specific responses, either excitatory or inhibitory (Figures 8C,D). To evaluate the population response, we calculated a dissimilarity index, which indeed demonstrate

that the AL discriminates between larva-damaged and intact plants (Figure 8E). Knowledge of the compounds that are discriminated at this level of olfactory processing readily informs about the suite of VOCs that could potentially mediate the behavioral selection of appropriate host plants.
<figure><img src="image_103.png" /><figcaption>**FIGURE 7** | Simultaneous GC trace (top) and single sensillum recording (bottom trace) obtained in response to stimulation with *M. sexta* larva-damaged tobacco (*Nicotiana attenuata*, A) or *D. wrightii* (B).</figcaption></figure>

Numbers indicate neuron responses to phenol (1), 4-methylpentanol (2), cis-3-hexanol (3), cis-3-hexenyl acetate (4), benzyl alcohol (5) and methyl-benzoate (6). For methods, see Späthe et al. (2013). Figure courtesy of Drs. S. Kesevan and S. Olsson.

## Olfactory Responses in Sexually Isomorphic Pathways and Interconnected Glomeruli

As described above, female specific neurons are involved in mediating female-specific behaviors such as oviposition (Roche King et al., 2000; Reisenman et al., 2004) and in some moth species, detection of male pheromones (Hillier et al., 2006). As in many insects, the orientation of males toward the female-specific sex pheromone is crucial for the species survival, and the role of male-specific neurons in mediating this behavior is well-established in many moth species (e.g., Anton et al., 1997; Berg et al., 1998; Lei and Hansson, 1999; Vickers and Christensen, 2003). Although it might be tempting to argue that sexually dimorphic pathways are particularly selective as they mediate fundamental behaviors related to reproduction, we found that PNs in sexually isomorphic glomeruli can also be highly specific. For instance, PNs in an identified glomerulus (glomerulus 35, which neighbors the sexually dimorphic glomeruli in both sexes, Figure 2B) are extremely selective and sensitive to another host plant volatile, cis-3-hexenyl acetate (Figure 4C), responding to concentrations <1 ppm (Reisenman et al., 2005). While the specific role of this VOC for behavior is not yet elucidated (although its production is augmented in larva-damaged plants; Hare, 2007), knowledge of the specific VOCs that activate specific sets of glomeruli has provided a tool to study interactions between glomeruli involved in mediating different behaviors. Previously, Lei and coworkers elegantly demonstrated that the temporal output of each male-specific glomerulus is enhanced by reciprocal inhibitory interglomerular interactions, and that this serves to synchronize the activity of neurons processing

the components of the sex pheromone blend (Lei et al., 2002). Using known odor inputs to activate specific glomeruli beyond the sex-specific system, we found that the two AL subsystems interact synaptically in a distant-independent, non-reciprocal fashion (Figure 4C, middle panel; Reisenman et al., 2008), and that these interactions are mediated by a functionally and morphologically heterogeneous population of local interneurons (Figures 2D,E; Reisenman et al., 2011). Interactions between odors with different behavioral significance have been also described in other moth species, both at a behavioral and at a neural level (e.g., Chaffiol et al., 2012, 2014; Deisig et al., 2012; Trona et al., 2013).

Experiments conducted in many insect species, including *M. sexta*, indicate not only that glomeruli interact synaptically, but that sets of interconnected glomeruli are likely involved in the processing of behaviorally relevant odor blends. At the AL level, this idea is supported by the fact that a sizeable proportion of LNs interconnect a restricted subset of glomeruli (Figure 2E; Reisenman et al., 2011). The existence of PNs that arborize in multiple -but restricted- glomeruli, also supports this hypothesis (Figure 2F). At the level of the chemical signals, it is known that some of the active compounds identified in the host plant headspace are ubiquitous floral and vegetative VOCs. Thus, it is possible that a suite of compounds presented in particular proportions (Thiery and Visser, 1986; Zhang et al., 1999; Riffell et al., 2008b, 2009a), rather than a single compound, activates a subset of glomeruli to mediate host plant selection. For instance, a blend of just three floral *D. wrightii* VOCs (but not any of the single VOCs) can elicit feeding (Riffell et al., 2009b). Similarly, the sole presence of linalool is not sufficient to mediate oviposition, although the presence of this component in plants has profound behavioral effects (Figure 5; Reisenman et al., 2010). Because different host plants are readily accepted for oviposition by females, it is possible that that individual VOCs shared across plant species activate a functionally connected glomerular subset (which necessarily involves at least some of the female-specific glomeruli), the output of which ultimately control oviposition behavior. The chemical composition of that bouquet, however, remains to be identified.

## Moths Find Plants, but How Do the Plants Impact the Moths?

While in the previous section we discussed the neural processing of naturally occurring signals and its consequences for behavior, in this section we highlight some plant cues and signals that in turn, can influence moth behavior. From the plant prospective, what matters is to attract efficient pollinators. In the case of *D. wrightii*, as we mentioned, this has the un-intended consequence of also attracting gravid females. This plant species can effectively cope with this, as plants can tolerate high levels of defoliation, quickly regrow after herbivory, reduce photosynthetic rates, and redirect resources to storage in the roots upon herbivory (cited in Reisenman et al., 2010). However, once moths probe flowers, other gustatory sensory cues present in nectar appear to have profound effects in guiding behavioral decisions.
As mentioned before, plants often produce secondary compounds (e.g., alkaloids, glycosides, and phenolic compounds) to deter herbivores and pathogens (Karban and Baldwin, 1997). Interestingly, these secondary compounds are also present in floral nectar (Adler, 2000) and can be induced by herbivory in a plant-species specific manner (Adler et al., 2006; Hare and Walling, 2006; Kessler and Halitschke, 2009). It has been suggested that the presence of these components in nectar increases pollinator fidelity, repel nectar robbers, and improve pollen transfer by intoxicating pollinators (Adler, 2000).

In the case of *D. wrightii*, an obvious advantage for female *M. sexta* is that visits to flowering plants provide both nectar and oviposition resources. However, because of the limited availability of the flowers (which bloom for only one evening) and the effects of herbivory (which limits the number of healthy host plants), female moths encounter resources that are spatio-temporally patchy. The moth nervous system has the ability to adjust its activity so that behavioral output is maximally beneficial for survival within an often harsh and patchy environment. This plasticity is accomplished by the release of specific neuromodulators—including serotonin, octopamine and dopamine—within restricted brain regions like the AL, the lateral horn and the mushroom bodies. For example, herbivory-induced damage to *D. wrightii* plants elicits high concentrations of certain alkaloids in the flower nectar which *M. sexta* finds aversive. Tropane alkaloids (including scopolamine) in the nectar of damaged *D. wrightii* increase more than 20-fold in damaged plants (Dacks et al., 2012). Over time, moths learn the association between the alkaloid content and the floral and vegetative scent so that these damaged plants become avoided. Dopamine release in the moth brain—including the ALs and the mushroom bodies—has shown to be a critical signal mediating aversive learning and signaling the presence of an aversive stimulus. For instance, when the ALs of female moths were injected with a dopamine receptor antagonist, moths could no longer learn the association of the aversive nectar and the flower scent. Furthermore, when dopamine was superfused on to the AL, the neural ensemble showed enhanced responses to the flower odor stimulus. Dopaminergic modulation of AL circuits thus plays an important role in the memory formation of repellent flower scents and the discrimination of larva-damaged plants. Interestingly, the effects of alkaloids in moth preference are shaped by both the plant species and the behavioral context. For instance, females prefer to oviposit in tobacco plants which have higher concentrations of nicotine in nectar (Adler et al., 2006), but they remove less nectar from these plants (Kessler and Baldwin, 2006). Thus, the nervous system can differentially evaluate the same plant sensory cue (in this case, nicotine) according to the behavioral context.

What are the effects of nectar secondary compounds on insect behavior? In general, naturally-occurring concentrations of secondary compounds do not deter nectar-feeding insects, whether specialists or generalists. In generalist insects such as bees, low concentrations of certain secondary compounds such as nicotine and caffeine elicit feeding preference (Singaravelan et al., 2005). This preference is not mediated by peripheral taste

receptors, but is probably due to the effect of these substances in reward brain centers (Singaravelan et al., 2005; Kessler et al., 2015). In contrast, prolonged exposure to high concentrations of these compounds (e.g., such as those found in flowering crops sprayed with neonicotinoid pesticides) can impair olfactory learning and memory (Williamson and Wright, 2013). The effects of these substances in learning and memory in herbivorous insects which are exposed to natural concentrations of plant secondary defenses have not been yet studied, but tobacco plants which have been engineered to completely lack nicotine in nectar have more nectar removed per night (Kessler and Baldwin, 2006). Unlike bees, taste receptors in the mouthparts of moths can readily detect bitter compounds such as caffeine (Bernays et al., 2002; Glendinning et al., 2006), which are commonly present in their host plants. The effects of these substances on behavior, however, remain to be investigated in the appropriate ecological and behavioral context.

## Summary and Conclusions

In the last couple of decades, research in the neuroscience field has focused on a small number of “model” species offering various advantages, at the expense of potentially creating a bottleneck which limits or compromises our understanding of how nervous systems operate (Brenowitz and Zakon, 2015). Today, several genome project efforts, and increasingly available tools that allow DNA editing, are bridging this gap. However, an integrative approach that includes ecological and community relationships, natural signals, neurons and behavior, has always been and it will always remain key to understand the function of nervous systems.

Here we used an exemplary specialized herbivorous insect, the moth *M. sexta*, to review the function of the moth's olfactory system in a naturalistic context. While floral odors attract moths for feeding and oviposition, volatiles released from larva-damaged plants mediate oviposition repellence. Specific plant volatiles are involved in mediating these behaviors, and are processed in both sexually isomorphic and dimorphic neural pathways according to the behavioral context (feeding and oviposition). Furthermore, some of these volatiles also mediate behavior in distant moth species, suggesting important roles for certain plant volatiles and commonalities in their neural processing. In addition, for the plant benefit, plant secondary compounds can affect host-plant finding and behavior through processes such as learning and memory.

While today we have a deeper understanding of how the nervous system process information about behaviorally relevant VOCs in *M. sexta* and other insect species, many issues, at several levels of interactions, need to be addressed. For instance, do different populations of host plants differ in their VOC profile, and what are the behavioral consequences? Given that *M. sexta* is found throughout a wide range in the American continent, is there a common set of VOCs that guide oviposition choice, despite that moths across the distribution range use different host plant species? Is there a minimum VOC blend that produces acceptance and egg lying? Would this blend suffice to guide oviposition choice in different moth populations? Are
A

<figure><img src="image_104.png" /></figure>

B

<figure><img src="image_105.png" /></figure>

C

<figure><img src="image_106.png" /></figure>

D

<figure><img src="image_107.png" /></figure>

E

<figure><img src="image_108.png" /></figure>

**FIGURE 8** | The response of many simultaneously recorded AL neurons to VOC extracts and single components can be studied using a multi-unit recording electrode coupled to gas-chromatographic analysis (GC-MRA). (A) Plant extracts are injected on the GC inlet; after leaving the GC column a Y-splitter divides the effluent

for simultaneous antenna stimulation and chemical identification of individual components. (B) GC-MRA analysis showing a neuron with robust responses to the larva-damaged VOCs responding selectively to only one peak in the GC effluent (arrow, cis-3-hexen-1-ol). (C) Simultaneously recorded
(Continued)
<caption>**FIGURE 8 | Continued**</caption>

responses of 4 units to stimulation (duration = 200 ms, gray bars) with the plant extracts indicated to the left. Shown are individual spikes (tick marks) during repetitive stimulation (rows), and the peri-event histograms calculated across trials (bottom). Note that different units respond differently to different stimuli, and that the same unit responds differentially to intact and larva-damaged extracts. (D)

Response indexes (or z-scores, color-coded, calculated as spiking rate during stimulation—spiking rate pre-stimulation/SD) for six simultaneously recorded neurons in response to stimulation with the extracts indicated. The first two units showed stronger responses to stimulation with larva-damaged plants. (E) The dissimilarity index (Riffell et al., 2009a) indicates stronger AL neuronal ensemble responses to larva-damaged plants.

populations in other regions as specialized in certain host plants as the Southwest USA population? Do background odors affect olfactory-guided oviposition choices? Do other factors such as temperature and humidity affect host plant choice? In nature, do females actually avoid ovipositing on plants where other larvae are already present, and if so, how do they achieve this? An interesting possibility, given that *M. sexta* host plants naturally produce alkaloids which can be readily detected by moths (Bernays et al., 2002; Glendinning et al., 2006), is that plants manipulate nicotine concentrations in nectar to their benefit, as these substances could act as postingestive stimulants and even have addictive properties, improving flower finding and efficiency (Singaravelan et al., 2005; Kessler et al., 2015). We know much about the “Neuroecology” of oviposition and feeding behavior in adult insects, but do larvae make choices within a plant, and how are these choices guided? Do larvae prefer younger or older leaves, small or big? Importantly, a comparative strategy has the power of help unraveling general neural mechanisms and strategies that guide host plant choice.

We propose that all these issues by necessity need to be investigated in an appropriate Neuroecology framework.

Finally, along with a deep understanding of the relationships of organisms with their natural environment, we believe that in the near future genomic tools now available to many insect species will permit a deeper and more complete understanding of how the insect nervous system produces adaptive behavior.

## Acknowledgments

We thank Drs. John Hildebrand and Kristin Scott for their support and encouragement, and to the two reviewers for their comments and suggestions that greatly improved this manuscript. CR and JR were initially supported by an award from NSF (IOS-0822709). JR was supported by awards from NSF (IOS-1354159 and DMS-1361145) and the Human Frontiers in Science Program (RGP0022). We thank Drs. S. Olsson and S. Kesevan for generously providing **Figure 7**, and Dr. J. Hildebrand for allowing the use of **Figure 2C**.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Reisenman and Riffell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
# Neuroethology of Olfactory-Guided Behavior and Its Potential Application in the Control of Harmful Insects

Carolina E. Reisenman¹, Hong Lei²* and Pablo G. Guerenstein³,‡

¹Department of Molecular and Cell Biology and Essig Museum of Entomology, University of California, Berkeley, Berkeley, CA, USA, ²Department of Neuroscience, University of Arizona, Tucson, AZ, USA, ³Lab. de Estudio de la Biología de Insectos, CICyTTP-CONICET, Diamante, Argentina, ⁴Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Oro Verde, Argentina

Harmful insects include pests of crops and storage goods, and vectors of human and animal diseases. Throughout their history, humans have been fighting them using diverse methods. The fairly recent development of synthetic chemical insecticides promised efficient crop and health protection at a relatively low cost. However, the negative effects of those insecticides on human health and the environment, as well as the development of insect resistance, have been fueling the search for alternative control tools. New and promising alternative methods to fight harmful insects include the manipulation of their behavior using synthetic versions of “semiochemicals”, which are natural volatile and non-volatile substances involved in the intra- and/or inter-specific communication between organisms. Synthetic semiochemicals can be used as trap baits to monitor the presence of insects, so that insecticide spraying can be planned rationally (i.e., only when and where insects are actually present). Other methods that use semiochemicals include insect annihilation by mass trapping, attract-and-kill techniques, behavioral disruption, and the use of repellents. In the last decades many investigations focused on the neural bases of insect’s responses to semiochemicals. Those studies help understand how the olfactory system detects and processes information about odors, which could lead to the design of efficient control tools, including odor baits, repellents or ways to confound insects. Here we review our current knowledge about the neural mechanisms controlling olfactory responses to semiochemicals in harmful insects. We also discuss how this neuroethology approach can be used to design or improve pest/vector management strategies.

**Keywords:** crop pest, disease vector, integrated pest management, odor attractant, disruption of behavior, odor repellent, insect neuroethology

## INTRODUCTION

Humans benefit from insects, mainly as pollinators of crops, but an important number of other insects are pests of crops or damage storage goods, are vectors of serious human and animal diseases, or are simply a nuisance. For centuries, humans have been fighting harmful insects, and the use of synthetic or genetically modified plant-produced chemical insecticides has made this fight much more efficient. However, the use and overuse of those chemicals has led to a number of undesirable consequences, such as contamination of our environment, food and water,

**OPEN ACCESS**

**Edited by:**
Sylvia Anton,
Institut National de la Recherche
Agronomique, France

**Reviewed by:**
Rickard Ignell,
Swedish University of Agricultural
Sciences, Sweden
Andrey Nikolaevich Frolov,
All-Russian Research Institute of Plant
Protection, Russia

*Correspondence:
Pablo G. Guerenstein
pguerenstein@bioingenieria.edu.ar

†Present Address:
Hong Lei,
School of Life Sciences, Arizona State
University, Tempe, AZ, USA

**Specialty section:**
This article was submitted to
Invertebrate Physiology,
a section of the journal
Frontiers in Physiology

Received: 02 February 2016

Accepted: 16 June 2016

Published: 30 June 2016

**Citation:**
Reisenman CE, Lei H and
Guerenstein PG (2016) Neuroethology
of Olfactory-Guided Behavior and Its
Potential Application in the Control of
Harmful Insects. Front. Physiol. 7:271.
doi: 10.3389/fphys.2016.00271
and insecticide resistance. In addition, the rising of the organic agriculture movement demands insecticide-free food (van der Goes van Naters and Carlson, 2006).

Chemicals other than insecticides can be used to fight insects through the manipulation of specific olfactory behaviors, profiting from the existence of natural compounds used for communication between organisms, the semiochemicals (Pickett et al., 1997). Pheromones are perhaps the most well-known class of semiochemicals. Pheromones mediate interactions between organisms of the same species, and include, sex, aggregation, and alarm substances, while allelochemicals are semiochemicals that mediate inter-specific interactions (see Dusenbery, 1992; Wyatt, 2003 for further details).

The potential use of semiochemicals to monitor, disrupt, lure, repel, confuse, or mass-trap insect pests was rapidly acknowledged and has fueled much research (Wyatt, 2003; Witzgall et al., 2010) with the promise of clean, safe, and highly specific pest and vector control tools. For instance, mating disruption, in which large amounts of a synthetic sex pheromone are released in a crop, has been used to eradicate insect pests that became resistant to pesticides (Wyatt, 2003; Witzgall et al., 2010). Semiochemicals can also be used for trapping insects in integrated pest and vector control management strategies. Thus, when trapping devices include insecticides, insects attracted to a semiochemical also pick up lethal substances or pathogens (a strategy known as "lure and kill"; Pickett et al., 1997; Wyatt, 2003).

In the last decades, many studies focused on the neural mechanisms underlying behavioral responses to semiochemicals. These investigations aid the design of odor-based strategies for insect control, as they help understanding how the olfactory system processes information about odors and also allow generating predictions about the insect's olfactory behavior (e.g., Hildebrand, 1996; Guerenstein and Hildebrand, 2008). Unfortunately, research in the fields of neuroethology and insect control has been often segregated, which may hamper the development of novel and efficient control tools and strategies. In light of this, here we review our current knowledge about the neural mechanisms controlling olfactory responses to semiochemicals in harmful insects, and also discuss how this neuroethology approach can be used to manipulate insect behavior and therefore improve pest/vector management strategies. We start by briefly summarizing the structure and function of the insect olfactory system.

## THE INSECT OLFACTORY SYSTEM

Olfactory receptor cells (ORCs) are the first neural elements in the olfactory pathway and are housed in variable numbers in hair-like, multi-porous structures known as olfactory sensilla. Olfactory sensilla are located mainly on the antennae and in some insects also in the mouthparts. After entering the sensillum through its wall pores, odors diffuse in the aqueous sensillum lymph (sometimes transported by odorant binding proteins, Vogt and Riddiford, 1981; Tsuchihara et al., 2005; Leal, 2013) and reach the dendrites of the ORCs. There,

odors interact with different classes of chemoreceptor proteins: odorant receptors (ORs), ionotropic receptors (IRs), or gustatory receptors (GRs; Vosshall et al., 1999; Larsson et al., 2004; Vosshall and Stocker, 2007; Vosshall and Hansson, 2011; Suh et al., 2014). Many ORCs respond to only one or a few related odor compounds, particularly when tested at behaviorally relevant and naturally-occurring concentrations, but others are more broadly tuned (e.g., de Bruyne et al., 1999; Hansson et al., 1999; Stranden et al., 2003; Yao et al., 2005; Hallem and Carlson, 2006; Martelli et al., 2013). In all cases their response spectra depends on the odor tuning of the chemoreceptor protein/s expressed (e.g., Hallem and Carlson, 2006; Andersson et al., 2015). Each type of ORC usually expresses only one type of OR, IR, or GR (e.g., Vosshall et al., 1999; Galizia and Sachse, 2010). However, in some ORCs more than one OR, IR, or GR types, and even different chemoreceptor protein types (most commonly ORs and IRs), are co-expressed, and in those cases odors interact with more than one chemoreceptor protein type (e.g., Fishilevich and Vosshall, 2005; Abuin et al., 2011; Rytz et al., 2013; Hussain et al., 2016; see below).

Odorant receptors are usually expressed in ORCs within single-walled (basiconic or trichoid) sensilla. They are part of a heteromeric complex consisting of an OR-subunit which binds the odor ligand (thus conferring odor specificity) and the highly conserved OR co-receptor (ORCO). Experimental evidence suggests alternative mechanisms of odor activation, one in which OR-ORCO forms a non-selective ligand-activated cation channel, and the other in which ORCO itself functions as a cation channel (Sato et al., 2008; Wicher et al., 2008). Although ORCO orthologs exist in many insect species, to date there is no agreement on how ORCO functions during olfactory transduction *in vivo* (Stengl and Funk, 2013).

ORCs that respond to compounds such as ammonia, short chain carboxylic acids and amines are housed in double-walled (grooved peg and coeloconic) sensilla (Pappenberger et al., 1996; Diehl et al., 2003; Benton et al., 2009; Hussain et al., 2016) and do not express ORs but instead IRs. The IRs form ionic channels activated by ligands (Benton et al., 2009) and are expressed with one or two broadly expressed co-receptors different from ORCO (Abuin et al., 2011; Ai et al., 2013; Rytz et al., 2013). In addition, the very volatile molecule CO₂, which is of primordial importance for the olfactory orientation of blood-sucking insects and some herbivores (Guerenstein and Hildebrand, 2008), is detected by two to three members of the GR family co-expressed in a single ORC type (Suh et al., 2004; Jones et al., 2007; Kwon et al., 2007; Lu et al., 2007; Kent et al., 2008; Wang et al., 2013).

The axons of the ORCs project to the first processing center of olfactory information in the insect brain, the antennal lobe (AL; e.g., Anton and Homberg, 1999). The AL, analogous to the vertebrate olfactory bulb, is composed of distinct spheroid structures called glomeruli (Anton and Homberg, 1999; Fishilevich and Vosshall, 2005). Usually, the terminals of ORCs expressing the same chemoreceptor protein converge onto a single glomerulus (Vosshall et al., 2000; Guerenstein et al., 2004a; Rytz et al., 2013; Suh et al., 2014; Hussain et al., 2016). Each glomerulus also houses neurites of local interneurons (LNs) and
of projection neurons (PNs). LNs are restricted to the AL and have dendritic arborizations in several glomeruli; PNs usually arborize in one glomerulus and have an axon that projects to higher brain areas in the protocerebrum such as the lateral horn, the inferior lateral PC, and the calyces of the ipsilateral mushroom body (Hornberg et al., 1988, 1989; Jefferis et al., 2007; Galizia and Rössler, 2010; Tanaka et al., 2012; Roussel et al., 2014). Neurons in these higher-order brain centers show diverse responses and integrate information about different odor compounds (e.g., Jefferis et al., 2007; Turner et al., 2008; Gupta and Stopfer, 2012; Lei et al., 2013); neurons receiving input from the mushroom body calyces are involved in mediating learning and memory processes (e.g., Davis, 2004; Fahrbach, 2006; Liu et al., 2012). Further downstream, circuits in the lateral accessory lobe and the ventral protocerebrum have been linked, particularly in moths, to important aspects of olfactory behaviors (e.g., Olberg, 1983; Iwano et al., 2010).

In the next sections we review current knowledge about the neural and behavioral mechanisms underlying responses to diverse classes of pheromones, host odors, and plant volatiles, mechanisms of olfactory repellence, disruption of olfactory behavior, and the effects of experience and learning in olfactory-driven behaviors.

# OLFACTORY ATTRACTION FOR MONITORING AND TRAPPING

## Use of Sex Pheromones

Pheromones are usually mixtures of several compounds. Thus, the development of synthetic pheromone-blend attractants as trap lures involves knowledge of the compound identities, their concentrations, and their relative proportions. In several sympatric moth species, females release sex pheromones of overlapping chemical composition but with species-specific compound ratios, suggesting that males use this information to find conspecific females. For instance, different strains of the European corn borer (Ostrinia nubilalis) are attracted to precise pheromone blend ratios (Klun et al., 1973). Similarly, different species of *Yponomeuta* moths, which feed on the same host and share the same three pheromone constituents, are reproductively isolated due to differential attraction to species-specific blend ratios (Löfstedt et al., 1991). Similar findings were also reported on aphids (Dewhirst et al., 2010) and plant bugs (Byers et al., 2013). While the importance of ratios is crucial for the design of trap lures, the neural mechanisms underlying this phenomenon just began to be understood (e.g., Martin et al., 2013).

Sex pheromones can be used for monitoring and trapping many insect species. While we review and discuss what is known across different insect species, much is known about the neurobiological bases of mate seeking and finding in the moth *Manduca sexta*. Knowledge gained through studies in this insect could be applied to other insect-pest species, particularly other moths, as it is likely that similar neural mechanisms underlie mate odor-guided seeking behavior.

In moths and cockroaches, information about the female sex pheromone is processed by a small number of male-specific

AL glomeruli forming a distinct structure, the macroglomerular complex (MGC; e.g., Boeckh and Boeckh, 1979; Hildebrand et al., 1980). Although the MGC sub-system of moths is distinctive and particularly large, the synaptic organization and structure of its constituent glomeruli is akin to that of the rest of the AL glomeruli. In some moth species, each MGC glomerulus processes a cognate pheromone component (e.g., *Heliothis virescens*; Berg et al., 1998), but in other species multiple components are encoded in the same MGC subcompartment (e.g., *Spodoptera littoralis*; Anton and Hansson, 1995). In other cases, pheromones and plant odorants are processed by the same MGC neurons (e.g., *Agrotis ipsilon*; Rouyar et al., 2015). Given this complexity, the use of simpler model systems (e.g., see next) can be experimentally advantageous and help the discovery of common, basic principles underlying the processing of complex odor blends.

The MGC of *M. sexta* has two main glomeruli, the Cumulus and the Toroid, each processing information about one of the two major female sex pheromone blend components (Hansson et al., 1991, 1992; Heinbockel et al., 1999). Because only these two components (out of eight total) are required to elicit odor-induced orientation behaviors in males (Tumlinson et al., 1989), this provides a simple binary system to investigate the neural mechanisms mediating pheromone processing, including blend ratio processing. When males are stimulated with the pheromone blend, two distinct populations of ORCs are specifically activated by those two essential components, one evoking excitatory responses in Cumulus projection neurons (cPNs) and the other in Toroid projection neurons (tPNs; Kaissling et al., 1989; Hansson et al., 1992; Hildebrand, 1996; Heinbockel et al., 1999; Lei et al., 2002). Additionally, recent findings suggest that cPNs and tPNs correlate their synaptic output to signal the presence of the pheromone blend (Lei et al., 2013; Martin et al., 2013). In principle, the odor-evoked spiking activity of cPNs and tPNs could serve to report the chemical identity and concentration of each blend component. However, since their outputs converge in the same regions in the protocerebrum (the delta region of the lateral horn and the mushroom body calyces), the relative timing of input spikes from cPNs and tPNs in postsynaptic neurons may have a physiological effect, that is, coincident spikes would evoke a stronger response in postsynaptic neurons than sequential spikes, allowing the representation of an odor mixture as a single odor object (see also Section Effects of Background Odor).

Indeed, using simultaneous dual-electrode intracellular recordings, Lei et al. (2002) showed inter- and intra-glomerular spike synchrony among PNs in response to pheromone blend stimulation. Odor-induced interglomerular synchrony in the AL was also reported in cockroaches using voltage-sensitive-dye imaging methods, suggesting that the synchrony code operates at a broad spatial scale (Watanabe, 2012). Moreover, experiments that simultaneously recorded neuronal activity across the glomerular array in *M. sexta* showed that neurons with the most similar odor response profiles produced the highest degree of coincident spikes (Lei et al., 2004). These results support the notion that PNs may use a correlative neural code. In addition, local field potential oscillations in the mushroom bodies, which
are thought to reflect evolving ensemble synchrony of PNs across the entire array of AL glomeruli, were reported in many insect species, including locusts, fruit flies, and moths (MacLeod and Laurent, 1996; Ito et al., 2009; Tanaka et al., 2009). Further, it has been shown that spike coincidence in *M. sexta* AL neurons is modulated by the pheromone blend ratio. Behaviorally, the moths respond best to the mixture of the two essential pheromone components at the naturally occurring 1:2 ratio, and deviations from this ratio deteriorate blend attractiveness (Martin et al., 2013). By stimulating AL neurons with varying blend ratios while simultaneously recording the activity of PN pairs, it was shown that MGC-PNs produce peak correlations at the natural 1:2 blend ratio, and those correlations significantly deteriorate in response to stimulation with behaviorally sub-optimal blend proportions (Martin et al., 2013). Such stimulus-quality-affected correlations in the PN spikes were also reported for glomeruli other than those of the MGC, in experiments that manipulated the ratios of naturally-occurring hostplant blends (Riffell et al., 2009a).

The mechanisms determining spike correlations are unknown, but balanced inhibition may be involved. Upon pheromonal stimulation, both PNs and LNs are activated, with cPNs and tPNs excited by their cognate pheromone constituents and reciprocally inhibited through GABAergic LNs (Lei et al., 2002). LNs likely respond in a dose-dependent manner, allowing the inhibitory effect exerted onto PNs to be modulated by the relative proportion of the blend constituents. Moreover, the degree of spike coincidence between PNs is positively correlated with the strength of the inhibitory input onto those PNs (Lei et al., 2002). Similarly, in the AL of cockroaches, GABAergic LNs also mediate synchronization of PN outputs (Watanabe, 2012). Thus, balanced lateral inhibition is a plausible mechanism by which stimulation with a pheromone blend of optimal ratio can produce the highest degree of correlated spikes in PNs. These ideas are yet to be experimentally confirmed, but have already been explored to some extent in a modeling study (Zavada et al., 2011). Given the diversity of LNs in the AL (Wilson and Laurent, 2005; Seki and Kanzaki, 2008; Reisenman et al., 2011), lateral inhibition may involve particular LN types. Indeed, a recent study on the silkmoth *B. mori* revealed the existence of both spiking and non-spiking LNs, and showed that non-spiking LNs can inhibit PNs (Tabuchi et al., 2015). Some of these effects may be species-specific, as spiking LNs in the AL of the cockroach *Periplaneta americana* can inhibit PNs (Warren and Kroppenburg, 2014), while non-spiking LNs (at least those surveyed) do not (Husch et al., 2009).

If the observed spike correlations are meaningful, then the correlated code should be read by postsynaptic neurons. Indeed, although rare, some lateral horn protocerebral neurons, which are known to receive direct input from AL neurons and thought to mostly mediate innate behaviors (e.g., Homberg et al., 1989; Anton and Homberg, 1999; Jefferis et al., 2007; Galizia and Rössler, 2010; Roussel et al., 2014; Kohl et al., 2015), produce the strongest response to the two-component pheromone blend presented at the naturally occurring ratio (Lei et al., 2013). Such correlation hypothesis is also supported by a recent study in *Drosophila melanogaster*. The odor-evoked spikes of PNs

innervating a particular glomerulus (DA1) are highly correlated and provide converging input to their target neurons in the lateral horn (Jeanne and Wilson, 2015). Although the ligand of DA1-PNs is a single pheromone compound (cis-vaccenyl acetate), these experiments demonstrate that synchrony between PNs (arborizing in the same glomerulus in this case) occur, and could be related to coincident detection in post-synaptic neurons (Jeanne and Wilson, 2015). The identity of other *Drosophila* volatile pheromone compounds, and their processing circuits, were recently reported, although it is not yet known which mixtures are behaviorally significant in this species (Dweck et al., 2015).

In summary, both behavioral and neurobiological data indicate that not just the identity of the sex pheromone constituents, but also the constituents' ratios, are of paramount importance in mediating natural behavior. The neural mechanisms underlying the coding of ratios, particularly at the higher brain level, are still not fully understood. Because responses to sex pheromone mixtures are often species-specific, those mixtures represent an effective way to control specific species, which is much preferable to the use of insecticides as these often affect non-target species.

## Use of Other Pheromones

In this section we focus on aggregation and alarm pheromones, since those are the only non-sex pheromone types that have been used to manipulate olfactory behavior. We will briefly review what is known for the major groups of harmful insects.

Aggregation pheromones promote conglomerates of individuals and are ubiquitous among arthropods, including many harmful species of beetles, moths, thrips, triatomines, locusts, mosquitoes, sand flies, and ticks (Wertheim et al., 2005; Sonenshine, 2006; Cook et al., 2007; Lorenzo Figueiras et al., 2009). Often, the decay, fermentation and pathogenesis associated with insect aggregations are the cause of important economic damage to crops and goods (Wertheim et al., 2005; van der Goes van Naters and Carlson, 2006). For instance, all throughout North America pine forests have been succumbing to massive bark beetle infestations that destroyed expanse forests and increase the risks of mudslides and forest fires (Chapman et al., 2012; Raffa et al., 2013). Beetle aggregation pheromones have been used for monitoring and mass-trapping, and also to recruit large number of insects on trap trees that are then destroyed (see Cook et al., 2007 for a review). A recent study used single-sensillum recordings to investigate the odor response profiles of ORCs in both sexes of the brown spruce longhorn beetle *Tetropium fuscum*. Interestingly, it was found that the responses to aggregation pheromones and plant volatiles are not completely segregated and can be synergized by the presence of volatiles indicative of host stress (MacKay et al., 2015).

While in general aggregation pheromones attract both sexes (Wertheim et al., 2005), in some species gravid females are attracted to a pheromone that induces aggregated oviposition. For instance, females of the sandfly *Lutzomia longipalpis*, which transmit leshmaniasis, use an oviposition aggregation pheromone which benefits the offspring of unrelated individuals by preventing fungal contamination of larval food (Wertheim
et al., 2005). *Culex quinquefasciatus* gravid females, which are vectors of filariasis and West Nile Virus (among others), are attracted to a pheromone released from maturing eggs in conjunction with an indole compound derived from grass infusions (Mboera et al., 2000; Logan and Birkett, 2007), and these components evoke electrophysiological activity from antennal ORCs (Mordue et al., 1992; Blackwell et al., 1993). In other non-insect arthropods such as ticks, which transmit Lyme disease, fecal components promote arrestment and aggregation, and tarsi contact chemoreceptors respond to some of these components (e.g., guanine) with extremely high sensitivity (Grenacher et al., 2001; Sonenshine, 2006). Such information about the most effective bioactive components can have practical applications for tick control. For instance, aggregation pheromones can be used together with an acaricide that when applied to vegetation or livestock kills ticks upon contact (Sonenshine, 2006).

Alarm pheromones inform or alert a conspecific about impending danger; they are highly volatile, disperse quickly, and do not persist long (see Napper and Pickett, 2008 for a review). They are released by a variety of glands and include compounds belonging to different chemical classes (e.g., terpenes, hydrocarbons, nitrogen compounds). In blood-sucking insects, alarm pheromones could be used as repellents. Bed bugs release alarm pheromones in response to injury and ant attacks, causing conspecifics to disperse (Levinson et al., 1974a). This alarm pheromone is species-specific to a certain extent, and consists of two major components detected by antennal sensilla (Levinson et al., 1974b; Reinhardt and Siva-Jothy, 2007; Olson et al., 2009). When disturbed, adult triatomines release an alarm pheromone mainly composed of isobutyric acid that repels conspecifics (Guerenstein and Guerin, 2004; Manrique et al., 2006; May-Concha et al., 2013; Minoli et al., 2013a,b), which could be used as a triatomine monitoring tool (Minoli et al., 2013b). Isobutyric acid is detected by ORCs in grooved peg sensilla on the triatomine antenna (Guerenstein and Guerin, 2001), likely through the action of an IR (Guidobaldi et al., 2014).

Alarm signals are also conspicuously present in other hemipterans of economic importance such as stink bugs. Heteropteran alarm semiochemicals often have a six-carbon skeleton (e.g., trans-2-hexenal) and have little species specificity (Napper and Pickett, 2008). Insects of economic importance in other orders that produce an alarm pheromone include thrips and aphids. The alarm pheromone of thrips reduces oviposition and causes larvae to fall from plants, and thus could be used to pull insects away from crops (Pickett et al., 1997). When aphids are attacked, they release an alarm pheromone (trans-β-farnesene; Bowers et al., 1972; Dewhirst et al., 2010; Vandermoten et al., 2012) that causes dispersion of other nearby aphids, including inter-specific responses across subfamilies (Napper and Pickett, 2008). This and other alarm aphid compounds have been used for controlling aphids in both greenhouse and field settings (Pickett et al., 1997; Dewhirst et al., 2010; Vandermoten et al., 2012).

Interestingly, sometimes a semiochemical can function as an alarm or an aggregation pheromone, depending on its concentration. This has been shown for trans-2-hexenal in

cockroaches (Napper and Pickett, 2008), and for isobutyric acid in the blood-sucking triatomine bug *Rhodnius prolixus* (Guerenstein and Guerin, 2004; Manrique et al., 2006; Minoli et al., 2013a). Thus, not only the compound identity needs to be considered in tools for insect control, but also its concentration and behavioral context. While aggregation and alarm pheromones could be used to manipulate the olfactory behavior of harmful insects, we just started to understand how these signals are processed, particularly at the peripheral level. Control strategies can certainly benefit from a deeper understanding of the neural mechanisms controlling these olfactory-driven behaviors.

## Use of Host Odors

Many insects that feed or oviposit on a host such as a plant or a vertebrate are pests of crops or transmit human and/or animal diseases. It is well-established that host odors, including CO2, are a key cue for host detection and orientation (van der Goes van Naters and Carlson, 2006; Guerenstein and Hildebrand, 2008; McMeniman et al., 2014; van Breugel and Dickinson, 2014; Reisenman and Riffell, 2015). Much work has been done on the attraction of harmful insects toward natural and synthetic host odors and its neurobiological bases (Guidobaldi et al., 2014 and references therein), information that sometimes has been used to develop odor baits for traps (e.g., Krockel et al., 2006; Ryelandt et al., 2011; Mukabana et al., 2012; Guidobaldi and Guerenstein, 2013). Importantly, manipulation of host-seeking behavior offers many opportunities to disrupt harmful insects. Insects usually respond to specific mixtures of host odorants, even when they include ubiquitous (including non-host) odorants (Bruce and Pickett, 2011). Even when some constituents of those odor mixtures are essential to evoke a behavioral response (e.g., Geier et al., 1996; Guidobaldi and Guerenstein, 2013), in some cases certain components could have redundant roles and therefore, could be removed without decreasing attraction (e.g., Cha et al., 2008). Moreover, key components could be replaced without affecting attractiveness (Tasin et al., 2007). The neurophysiological bases of this phenomenon are not clear, but it is possible that in certain cases key odorants are detected by broadly tuned ORCs (that is, the same ORC could be involved in the detection of several behaviorally redundant key odorants). Thus, studies on the physiological responses of ORCs can have important implications for the design of attractive odor baits. Indeed, ORCs detecting different constituents of a natural odor mixture are sometimes co-localized in the same sensilla (Stensmyr et al., 2003). This, along with the finding that sometimes ORCs within a single sensillum interact (Nikonov and Leal, 2002; Ochieng et al., 2002, Su et al., 2012), makes possible the simultaneous detection and processing of mixture components already at the peripheral level.

As a general rule, odorant identities in the AL are encoded in spatial patterns of glomerular activation (Carlsson et al., 2002; Hansson et al., 2003; Wang et al., 2003; Lei et al., 2004), with some glomeruli narrowly tuned to certain odorants, including hostplant volatiles. For instance, PNs in a specific glomerulus of the *M. sexta* AL are extremely sensitive and narrowly tuned to the plant volatile cis-3-hexenyl acetate (Reisenman et al.,
2005). Moreover, other PNs in a female-specific glomerulus can discriminate, with high sensitivity, the (+) and (−) enantiomers of linalool (Reisenman et al., 2004). PNs in sexually isomorphic glomeruli, in contrast, are equally responsive to both enantiomers of linalool (Reisenman et al., 2004). Interestingly, these neurophysiological findings served to predict behavioral responses that were readily tested. Thus, later studies found that the two enantiomers of linalool respectively mediate oviposition attraction and repellence (Reisenman et al., 2010, 2013), and that these two compounds are equally effective in mediating feeding (Reisenman et al., 2010).

Different features of host odor blends are encoded in glomerular activity patterns. For instance, the encoding of odor mixture identity involves synchronous firing of PNs throughout the activated glomeruli, which may serve to “bind” the components of the odor mixture (Riffell et al., 2009a,b). In addition, stimulation with an odor mixture can evoke a glomerular activation pattern which is different from that evoked by the summation of the activity patterns evoked by each component (see below). The importance of ratios in the detection of host odor mixtures has been shown in different insects (e.g., Najar-Rodriguez et al., 2010; Guidobaldi and Guerenstein, 2016). In oriental fruit moths, for instance, particular ratios within a synthetic plant odor mixture affected oviposition attraction negatively. Corresponding neurophysiological studies found that information about component ratios occurs non-uniformly across AL glomeruli, and that further processing takes place in higher-order brain centers (Najar-Rodriguez et al., 2010).

As mentioned above, insects usually respond to specific host odor mixtures (e.g., Geier et al., 1999a; Barrozo and Lazzari, 2004a; Krockel et al., 2006). For example, triatomines are sensitive to various human compounds (e.g., CO₂, lactic acid, ammonia, carboxylic acids; Guerenstein and Lazzari, 2009), and a mixture of ammonia, lactic acid, and pentanoic acid evokes attraction, whereas there is low or no attraction to the single constituents (Guidobaldi and Guerenstein, 2013). Furthermore, in aphids, individual constituents of an otherwise attractive blend can have repellent effects (Webster et al., 2010). Some constituents of host odor mixtures can act synergistically to evoke attraction (e.g., Bosch et al., 2000; Barrozo and Lazzari, 2004a; Smallegange et al., 2005; Piñero et al., 2008; Guidobaldi and Guerenstein, 2013). In females of the oriental fruit moth *Cydia molesta*, minute amounts of benzonitrile added to an unattractive mixture resulted in a mixture that is as attractive as a natural blend. At the AL level, this bioactive mixture evoked strong activation and synergistic effects in an additional glomerulus not activated by the unattractive mixture (Piñero et al., 2008). Besides synergistic phenomena, additive effects in response to odor mixtures are also found at the central level (e.g., Lei and Vickers, 2008). Therefore, multi-component odor baits will likely be more attractive than single odorants, as they may form specific and reliable “odor objects” (e.g., Späthe et al., 2013, see Section Effects of Background Odor). Interestingly, it has been proposed that just a few (sometimes just three) key components of an odor blend are sufficient for reliable host recognition, even when the insects can detect a higher number of host odorants (Qiu et al.,

2007; Riffell et al., 2009a; Guerenstein and Lazzari, 2010; Bruce and Pickett, 2011; Guidobaldi and Guerenstein, 2013).

CO₂ is a food and/or oviposition host cue used by some herbivorous and hematophagous insects (Guerenstein and Hildebrand, 2008). Glomerulus-specific CO₂ PNs in the AL of *M. sexta* can follow high frequency CO₂ pulses, suggesting that these PNs report information about long-distance CO₂ cues (Guerenstein et al., 2004a). This idea is also supported by the finding that nectar-rich flowers emit relatively high levels of CO₂ (Guerenstein et al., 2004b). In fact, foraging moths use floral CO₂ as a long-distance cue to find those flowers (Thom et al., 2004; Goyret et al., 2008). This and other examples (e.g., van Breugel et al., 2015) again show that neurobiological studies can predict behavior, and ultimately can inspire odor-based control strategies (van der Goes van Naters and Carlson, 2006). The fact that blood-sucking insects are proving difficult to control (Logan and Birkett, 2007), and that they transmit an ever increasing number of diseases to humans and animals, emphasizes that further studies are needed to develop effective tools for insect behavioral manipulation. It should be noted that any odor-based control strategy should consider that different types of natural odor stimuli (including background odors) often interact (e.g., Chaffiol et al., 2012, 2014, see also Section Effects of Background Odor). In addition, it should be considered that the physiological state of the insects (e.g., mating, feeding) as well as learning affects their responses to odors (e.g., Barrozo et al., 2010; Saveer et al., 2012; Reisenman, 2014; Matthews et al., 2016; Section Plasticity in the Responses to Semiochemicals).

## Combined Use of Pheromones and Plant Volatiles

When insects detect a mate, their olfactory system is confronted with not only sex pheromones, but also background odors such as plant volatiles. In principle, sex pheromones admixed with green leaf volatiles should be very attractive to phytophagous insects because such mixture may indicate the presence of a calling mate in a proper context. Therefore, at least in certain cases, it would be important to include hostplant volatiles in sex pheromone traps. For instance, in the case of the codling moth *Cydia pomonella*, addition of plant volatiles [e.g., (E)-β-farnesene] to the sex pheromone (codlemone) significantly increased the proportion of males flying to the pheromone in wind tunnel experiments (Schmera and Guerin, 2012; Trona et al., 2013). In addition, it has been shown that females of the Egyptian cotton leafworm *S. littoralis* exposed to cotton volatiles start calling earlier than females exposed to non-host volatiles, and that mating pairs exposed to these volatiles start mating earlier. Also, more males reach (or arrive nearby) the pheromone source when hostplants, rather than non-hosts, are present (Binyameen et al., 2013).

Integration of sex pheromone and plant volatile information may occur at the peripheral level. For example, in the noctuid moth *Agrotis ipsilon* pheromone ORCs can be directly excited by plant volatiles (Rouyar et al., 2015). Moreover, in pheromone-specific ORCs of *Helicoverpa zea*, stimulations with binary mixtures of sex pheromone and single hostplant odorants [either linalool or (Z)-3-hexenol] produce stronger responses than
stimulation with the sex pheromone alone due to interactions between ORCs (Ochieng et al., 2002). Mixtures containing pheromone and plant odorants can also have a suppressive effect. For instance, in *S. littoralis*, herbivore-induced plant odorants can directly suppress the response of pheromone-specific ORCs (Hatano et al., 2015). Direct suppression has also been observed in *Heliothis virescences* males upon stimulation of pheromone-specific ORCs with a sex pheromone component and a number of plant volatiles (Pregitzer et al., 2012). Suppressive effects can also be due to interactions between ORCs (Andersson et al., 2010). Interestingly, in woodboring beetles (*T. fuscum*), some ORCs respond specifically to their aggregation pheromone, although other ORCs specifically respond to the aggregation pheromone combined with at least one plant compound (MacKay et al., 2015).

The olfactory sub-system dealing with the processing of sex pheromone signals has traditionally been considered as a specialized system different from the “main” olfactory sub-system dealing with the processing of host/food odors. This notion was strongly supported by the identification of pheromone-specific ORCs (Bray and Amrein, 2003; Mitsuno et al., 2008; Krieger et al., 2009; Grosse-Wilde et al., 2010; Montagné et al., 2012; Zhang et al., 2015) which in some insect species (particularly within Lepidoptera) project to a small but distinct number of male-specific glomeruli (the aforementioned MGC; Kanzaki and Shibuya, 1983; Christensen and Hildebrand, 1987; Hansson et al., 1992, 1995, 2003; Berg et al., 1998; Rospars and Hildebrand, 2000; Masante-Roca et al., 2002; Sadek et al., 2002; Lei et al., 2004). In spite of this anatomical and often functional separation, it is clear that the two olfactory sub-systems also interact at the AL level. Both suppressive and additive interactions between pheromone and plant odorants have been reported in the MGC of different Lepidoptera species. In some cases, suppressive effects were observed (Chaffiol et al., 2012; Deisig et al., 2012), while in others responses were enhanced (Namiki et al., 2008). The responses of neurons in sexually isomorphic glomeruli can also be affected by the presence of female pheromones in several species, but showed more interspecific variations (Namiki et al., 2008; Chaffiol et al., 2014). Moreover, in *C. pomonella*, both response enhancement and suppression in response to mixtures of pheromones and plant odors has been observed in sexually dimorphic and isomorphic glomeruli, respectively (Trona et al., 2013). Interactions between the two sub-systems are not necessarily reciprocal or determined by spatial proximity (Namiki et al., 2008; Reisenman et al., 2008; Trona et al., 2013). Furthermore, additive effects for single and pulsed stimulations with mixtures of pheromone and plant odors have been reported (Chaffiol et al., 2014). Because in most cases ORCs that respond to plant odorants do not respond to sex pheromones (and are located in different sensilla), the responses of AL neurons to sex pheromones in sexually isomorphic glomeruli likely result from AL network interactions (Reisenman et al., 2008; Deisig et al., 2012; Chaffiol et al., 2014). The processing of combined signals (i.e., pheromone and non-pheromonal) in higher brain centers is less understood, but it is likely that neurons in these centers further contribute to this interaction.

All these results, both at the peripheral (ORC) and AL level challenge the traditional idea that pheromone and hostplant odor reception and processing are segregated. Thus, these results indicate that olfactory neural circuits are perhaps far more functionally diverse than previously thought. At the same time, these findings highlight the idea that in order to develop efficient tools to manipulate mate-finding behavior it is important to consider the odor context of that signal (e.g., if appropriate for the species, pheromonal baits could also include a host odor).

Visual cues play important roles in modulating the olfactory behavior of insects (e.g., Green, 1986, 1993; Cardé and Gibson, 2010; Willis et al., 2011; Gaudry et al., 2012; McQuate, 2014; van Breugel et al., 2015), and thus, visual cues are often added to odor baits in traps (e.g., Green, 1994). As integration of visual and olfactory stimuli at the CNS has already been documented (e.g., Balkenius et al., 2009), further studies in higher brain centers could help improve the development of multimodal baits. Even when this integration of information is relevant for the manipulation of olfactory behavior, it exceeds the aim of this review, and will not be discussed here.

## Effects of Background Odor

Odor mixtures are thought to be represented in the insect brain as single “odor objects,” so that the unique mixture identity prevails over the information about its constituents (Lei and Vickers, 2008; Wilson and Sullivan, 2011; Stierle et al., 2013). When odor baits (usually odor mixtures) are used in the field for insect monitoring and control, they are necessarily presented against an odorous dynamic background (another odor mixture/s). Background odors can either be irrelevant, “mask” the target odor (making it unrecognizable), or can enhance the response to a target odor (Schroeder and Hilker, 2008). In principle, it is conceivable that the bait (target) plus the background odor are perceived as a single mixture, creating a new and emergent “odor object” that can interfere with the identification of the target odor. If that were the case, how do insects orient toward natural odor sources such as hosts, mates, and oviposition sites? In this section we review the importance of background odors in shaping the responses to a target odor bait.

Detecting and discriminating a target odor mixture requires binding its different components (e.g., Deisig et al., 2006; Riffell et al., 2009b), and this “odor object” should be salient even in the presence of background odors. How do nervous systems accomplish this task? In rats, prolonged odor stimulation leads to fast habituation of neurons in the olfactory cortex, so that new odors evoke clear, distinct, responses. As a result, when the two odors are present, the constant odor (background) is filtered while the target odor evokes a neural response, suggesting that animals can separate the target stimulus from its background (Kadohisa and Wilson, 2006; Linster et al., 2007). This idea is also supported by experiments in honeybees, in which odorants presented simultaneously (simulating components of a single odor source) were represented as a single object, while odorants presented with an inter-stimulus delay were represented separately (Szyszka et al., 2012; Stierle et al., 2013). Although interglomerular inhibitory interactions contribute to bind components into a single odor object (e.g., Deisig et al.,
2006; Riffell et al., 2009b; Stierle et al., 2013), it has been shown that asynchronous mixtures activate more inhibitory interactions than synchronous mixtures (Stierle et al., 2013). How could this target-background object separation happen in natural odor plumes? Since insect ORCs can have short (<2 ms) response latencies, the thin filaments of target odors that intermingle with those of background odors could be resolved temporally, thus allowing target-background odor segregation (Szyszka et al., 2014).

Convincing and exciting experiments in moths showed that constant odor backgrounds that are chemically different from the target odor do not affect the representation of the target odor, whereas backgrounds that contain a constituent in common with the target odor do (Riffell et al., 2014), a phenomenon akin to the masking effect reported in mosquitoes and other insects (Logan et al., 2008; Schroeder and Hilker, 2008, see Section Odor Masking). Background odors with a constituent in common with the target evoke a change in the balance of excitation and inhibition in AL neurons with respect to the response to the target odor alone, thus altering the representation of the target odor (Riffell et al., 2014). Pre-exposure to this type of background odors produces an exacerbated change in the response to the target odor, resulting from neurons being adapted to the common constituent (Riffell et al., 2014). Stierle et al. (2013, see above) used a different insect species and different experimental conditions, although also tested dissimilar target-background odors presented simultaneously, and arrived to different conclusions (Stierle et al., 2013). These authors found that this mixture is represented as a single distinctive odor object, while Riffell et al. (2014) reported efficient target-background discrimination.

Still, there is an experimental situation that has not been tested yet: similar target-background odors (or target and background with a common blend constituent) presented asynchronously. Because in nature background odor plumes can have a different temporal structure than target odor plumes, insects could exploit these temporal differences to segregate a target odor from its background, even when these have common constituents (Stierle et al., 2013; Szyszka, 2014; Rusch et al., 2016). Experience may also help this segregation, as learning increases the distinction between different scents (Fernandez et al., 2009; Riffell et al., 2013). While in the work described synthetic blends were used, it would be most informative to use complete natural blends as targets since in principle, it should be easier to alter the neural representation of a synthetic mixture consisting of just a few constituents than that of a multi-component natural odor. Somewhat related to this idea, it has been suggested that redundant odor blends reduce uncertainty as they convey more robust information (Wilson et al., 2015).

As mentioned above (Section Combined Use of Pheromones and Plant Volatiles), plant odors could influence the response to pheromones both at the peripheral and the AL levels. Moreover, suppression of attraction to the sex pheromone by hervivore-induced plant volatiles has been reported in *S. littoralis* (Hatano et al., 2015). However, *H. virescens* males can be effectively attracted to the conspecific female sex pheromone in a constant background of naturally-occurring hostplant

odors, including hervivore-induced plant volatiles (Badeke et al., 2016). While these results parallel those reported by Riffell et al. (2014), the attraction of *H. virescens* to the female pheromone is impaired in a background of high and supra-natural plant odor concentrations (Badeke et al., 2016). These results not only further underlie the importance of using natural, realistic stimuli, but also that additional studies are necessary to fully understand the mechanisms underlying target/background discrimination, as the chemical identity of the odors used, as well as the species under study, could certainly influence the results.

A particular constituent of the volatile background, CO2, also affects the behavior of at least some insects (Guerenstein and Hildebrand, 2008). Information about this odor cue is processed as information about other odors, while the background level of CO2 is simultaneously encoded (Guerenstein et al., 2004a). In hematophagous insects this cue is used to detect and find vertebrate hosts (e.g., Geier et al., 1999b; Barrozo and Lazzari, 2004b), while in moths it is used to detect and find oviposition sites and nectar resources (Stange, 1997; Thom et al., 2004; Goyret et al., 2008). While those CO2 sources evoke clear responses from the CO2 ORCs at natural CO2 background levels, higher CO2 background levels interfere with those responses (Guerenstein and Hildebrand, 2008). In mosquitoes, an elevated CO2 background impedes take-off and source contact by masking the stimulus signal (Majeed et al., 2014). Moreover, the oviposition behavior of *Cactoblastis cactorum*, a moth particularly sensitive to CO2, is also affected by elevated CO2 backgrounds (Stange, 1997) because ORCs stop firing at such high CO2 levels (Stange et al., 1995). However, the behavior and ORC responses of *M. sexta* moths are not affected by moderate increases in CO2 background levels, but instead by high-amplitude CO2 oscillations (Abrell et al., 2005). In addition, certain background odorants can modulate the activity of CO2 ORCs (e.g., Guerenstein et al., 2004a) or even evoke a response *per se* in those receptors (Turner et al., 2011), thus interfering with CO2-mediated behaviors.

In conclusion, the odor background can affect responses to target odors (e.g., Büchel et al., 2014). Thus, for example, efficient odor baits developed in the laboratory could fail to attract insects under field conditions, where different background odors are present. Although more research is needed to understand its role in insect behavior, the odor background should be taken into account when planning an odor-based pest/vector management strategy. In addition, it would be important to investigate the feasibility of techniques to disrupt natural olfactory behavior using masking (see Section Odor Masking) and/or background odorants, as this could improve the methods currently used to disrupt behavior using natural odorants (see Section Disruption of Natural Olfactory Behavior).

## OLFACTORY REPELLENCE

According to Barton-Browne (1977) a repellent is “a chemical that acting in the vapor phase prevents an insect from reaching a target to which it would otherwise be attracted.” A repellent