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Oecologia

, Volume 184, Issue 1, pp 151–160 | Cite as

Tuned in: plant roots use sound to locate water

  • Monica Gagliano
  • Mavra Grimonprez
  • Martial Depczynski
  • Michael Renton
Behavioral ecology –original research

Abstract

Because water is essential to life, organisms have evolved a wide range of strategies to cope with water limitations, including actively searching for their preferred moisture levels to avoid dehydration. Plants use moisture gradients to direct their roots through the soil once a water source is detected, but how they first detect the source is unknown. We used the model plant Pisum sativum to investigate the mechanism by which roots sense and locate water. We found that roots were able to locate a water source by sensing the vibrations generated by water moving inside pipes, even in the absence of substrate moisture. When both moisture and acoustic cues were available, roots preferentially used moisture in the soil over acoustic vibrations, suggesting that acoustic gradients enable roots to broadly detect a water source at a distance, while moisture gradients help them to reach their target more accurately. Our results also showed that the presence of noise affected the abilities of roots to perceive and respond correctly to the surrounding soundscape. These findings highlight the urgent need to better understand the ecological role of sound and the consequences of acoustic pollution for plant as well as animal populations.

Keywords

Foraging behavior Hydrotropism Moisture sensing Bioacoustics Directional root growth 

Introduction

All living organisms have basic needs and can only survive in environments where vital resources are available for those needs to be met. Water is one of those essential resources and its availability plays a critical role in terrestrial ecosystems where it strongly influences abundance, spatial distribution and species interactions of a wide range of plant and animal groups (Hawkins et al. 2003; McCluney and Sabo 2009; McCluney et al. 2012; Ledger et al. 2013). Because water is often limited and can be unevenly distributed across time and space, both animals and plants have evolved a number of morphological and physiological traits as well as behavioral strategies to cope with water scarcity and avoid dehydration. Ultimately when faced with water scarcity, both animals and plants have two main options: water-saving or water-seeking.

Several animals and plants have evolved to cope with water scarcity through their impressive physiological capacity to save previously acquired water (e.g., camels, Bekele et al. 2013; cacti, Niklas 1997). Taken to an extreme, bryophytes like the so-called ‘resurrection plants’ can remain in a dried state for years and then, rehydrate and return to a fully functional state within 48 h of rain (Scott 2000). These and many other morphological and physiological adaptations are clearly useful water-saving or desiccation-tolerance mechanisms; in general, however, the most common strategy to economize on water involves changes in behavior. By reducing rates of mobility during the hottest hours of the day, animals are able to minimize water loss. In plants, changes in the orientation of leaves (i.e., paraheliotropism) to avoid light and reduce leaf temperature can also substantially increase water use efficiency (Bielenberg et al. 2003).

Despite the incredible water-saving abilities displayed by many species, the majority of animals and plants are not physiologically well-equipped to survive long periods of drought without searching for new water sources. Consequently, they have evolved water-seeking behaviors to deal with and actively search for more suitable (wetter) environments (animals, McCluney and Sabo 2009; plants, Kiss 2007; Cassab et al. 2013). To actively search and find water, both plants and animals must rely on information of various kinds to make the most efficient directional decisions. Animals are known to use an array of multisensory orientation systems, which may include visual, auditory, olfactory, magnetic, hygrotactic, anemotactic, polarotactic and other cues (Bernáth et al. 2004; Russell et al. 2014). Plants are also known to be exquisitely sensitive to a wide range of environmental cues including geomagnetic fields and moisture gradients, which they use to direct their roots through the soil once a water source is detected (Hart 1990). However, how plants sense and are able to move in the direction of water in the absence of a moisture gradient still need to be elucidated.

Investigations addressing this fundamental question on the water-seeking abilities of roots have been extremely limited (Cassab et al. 2013). The main reason for this paucity is that hydrotropism is considered a “weak” tropism relative to other tropisms such as phototropism and gravitropism (for example, experiments have been performed in space in order to mitigate the overwhelming effects of gravity and gravitropism; Wolverton and Kiss 2009; Kiss et al. 2012). Nevertheless, this paucity is perplexing given that this behavior is critical to the way plants acquire water and hence survive. Here, we tackled this issue by examining the water-finding ability of roots of pea seedlings (Pisum sativum), a model species that has previously been used for assessing root hydrotropic response to moisture gradients (Jaffe et al. 1985). Recent work has found that the roots of corn seedlings are able to detect sound waves or vibrations and selectively use them for orientation (Gagliano et al. 2012a). As sound travels readily and far in dense environments such as soil, a plant’s ability to detect vibrations may represent a very efficient, yet hitherto unexplored, way of capturing information from distant sound sources for orientation towards water. For example, roots may detect noise emanating from water moving through the soil or flowing through natural channels or human-made structures such as underground pipelines used in water supply networks and sewer systems. As a matter of fact, the invasion of sewer pipes by tree roots is an all too common and costly issue in municipalities around the world (United States Environmental Protection Agency 1999; Östberg et al. 2012; Xie et al. 2014), yet no research has been directed towards better understanding plant hydrotropic behavior in the context of bioacoustics. Whether acoustic cues are really contributing to root orientation towards a water source available in the soil remains to be seen, however. Therefore, in this study, we used a custom-designed Y-maze (cf. Fig. 1) to investigate how roots locate a water source, by experimentally testing how the behavior of roots responds to different acoustic cues. The study consisted of a series of test scenarios (TS) where roots effectively ‘chose’ between different treatments applied to the ends of the Y-maze. Specifically, we experimentally investigated how roots choose the direction that correctly leads them to water by testing their response to the sound of water moving inside a pipe (Experiment 1) and then using playback experiments to test whether roots respond to sound recordings of water (Experiment 2). We also used recordings to determine whether roots were able to discriminate between water and other sounds when these co-occur (Experiment 3).
Fig. 1

Schematic representation of the custom-designed experimental Y-maze, made of a PVC pipe filled with soil and attached to two tightly fitting small black plastic pots and two transparent rectangular plastic trays at each lower end. Not to scale

Materials and methods

Germination, growth conditions and Y-maze design

Seedlings of the garden pea (Pisum sativum cv Massey Gem) to be tested in the Y-maze trials were germinated hydroponically in 250 mL round containers. Seeds were firstly soaked in water for 24 h and then wrapped with clean wet paper-towel and an external layer of aluminum foil. Five seeds per roll were used and seed rolls were placed vertically in a round container, immersed in 50 mL of water (replenished daily) and incubated in a dark germination chamber at 24 °C ± 0.1 (SE) average temperature and 62% ± 0.3 (SE) average humidity (simultaneously recorded using a HOBO data-logger). Progress towards germination was evaluated each morning and seeds were considered to have germinated when the radicle was >5 mm long. Upon germination, each seedling was planted in the center of its individual custom-designed Y-maze (Fig. 1) at a depth of approximately 25 mm, after the maze had been filled with soil (Osmocote seed raising and cutting mix) and the soil had been saturated with water and then allowed to drain freely for about 5–10 min (after which water stopped draining). Each maze consisted of an inverted Y-shaped PVC pipe fitted with two tightly fitting small black plastic pots (55 mm diameter by 47 mm in depth) and two transparent rectangular plastic trays (90 mm × 70 mm × 40 mm) at each lower end. Each maze was secured to a polyurethane foam base and placed into a plastic planting tray. Each seeded maze was randomly allocated to a test scenario (details below). To ensure similar growth conditions across test scenarios, seeded mazes were haphazardly distributed in a small (2 m × 4 m) glasshouse at the University of Western Australia Botany Glasshouse complex, and then left to grow undisturbed under natural light conditions for 5 days. For the entire duration of the experiments, the glasshouse was temperature-regulated by ventilation fans and automated shade-screening systems and its environment was monitored and recorded at 15-min intervals by a sensor located in the center of the room [average temperature 23 °C ± 0.1 (SE); average relative humidity 50% ± 0.3 (SE)]. In addition, air temperature was measured three times daily by two probes, randomly positioned adjacent to the seeded mazes inside the glasshouse [average temperature: 24 °C ± 0.2 (SE)]. Sunlight in the glasshouse was recorded every 30 min by a roof-mounted sensor [average ambient light level: 356 μmol m−2 s−1 ± 5.7 (SE)]. A total of 89 seeded mazes were included in the study.

Experiment 1: root directional responses to the sound of water inside a pipe

The aim of this experiment was to investigate whether acoustic cues, and specifically the sound of moving water, contribute to the hydrotropic behavior of roots. In test scenario 1 (TS1) (see Table 1, n = 10; Fig. 2a), we established the extent of root directional growth towards an actual water gradient produced by the presence of 100 ml of water contained in the transparent plastic tray at the base of one side of the maze [WTR]. In this baseline scenario, the water source was located on one side of the maze only (A) versus nothing applied to the other side (B). The A side was designated as the treatment side and assigned to the left or right side at random for each replicate maze. On day 5, the small black pots at the base of the maze were removed to expose the position of the primary root in the maze [i.e., left (L) versus right (R) side, (A) vs (B) treatment; Fig. 3a] and the test terminated. At the end of each test, the soil was gently washed out of the maze to reveal the distribution of the root system (primary root and lateral roots) within the maze while preventing damage. The position of the primary root was recorded and seedlings were then carefully extracted from the maze and photographed against it (Fig. 3b).
Table 1

Treatments applied in each test scenario, together with the expected direction of each potential hypothesized effect in each scenario: acting towards (1) or against (−1) root growth in the A treatment direction, or having no influence (0)

Exp

TS

Treatment A

Treatment B

Hypothesized effects

Water contact

Water presence

Sound

Water sound

White noise

Equipment presence

Equipment on

1

TS1

Water (WTR)

Nothing

1

1

0

0

0

0

0

TS2

Sound of water through pipe (WTR-PIPE)

Nothing

0

1

1

1

0

0

0

2

TS3

Recorded sound of water (WTR-REC)

Nothing

0

0

1

1

0

1

1

TS4

White noise (NOISE)

Nothing

0

0

1

0

1

1

1

TS5

Zero Hz (ZERO HZ)

Nothing

0

0

0

0

0

1

1

TS6

Sound equipment on but not playing (NOT PLAYING)

Nothing

0

0

0

0

0

1

0

3

TS7

Recorded sound of water (WTR-REC)

Water (WTR)

−1

−1

1

1

0

1

1

TS8

Recorded sound of water (WTR-REC)

White noise (NOISE)

0

0

0

1

−1

0

0

TS9

Recorded sound of water (WTR-REC)

Zero Hz (ZERO HZ)

0

0

1

1

0

0

0

When an effect is acting on both sides equally, such as equipment in TS8 and TS9, it is assumed to have no net influence towards or against root growth in the A treatment direction (0)

Fig. 2

Schematic representation of experimental treatments, where a water was directly accessible (WTR) or b present inside the tubing but not accessible (WTR-PIPE), and where c the recorded sound of water (WTR-REC), computer-generated white noise (NOISE) or no sound (ZERO Hz) were played back using a small MP3 player and speaker. Not to scale. a and b were assigned to left or right side at random for each maze

Fig. 3

Photographic record of the position of the primary root recorded on day 5. Firstly a the two small black pots at the base of the maze were removed to expose the position of the primary root in the maze (indicated by the red arrow) and then b seedlings were extracted from the maze to expose the primary root (indicated by the red arrow) relative to the whole root growth (color figure online)

In test scenario 2 (TS2) we examined the directional behavior of roots in response to one discrete test scenario in which seedlings had no direct access to water (Table 1, n = 10; Fig. 2b). In this scenario, the treatment consisted of the live sound of water running through clear PVC flexible tubing wrapped around one of the two plastic pots at the base of each maze with water continuously re-circulated by an SP-980 aquarium pump [WTR-PIPE]. We compared the observed frequency of seedlings directing their primary root towards the sound of water in this scenario relative to the expected frequency represented by seedlings in the baseline WTR group from TS1.

An additional separate experiment was also conducted to establish whether the presence of circulating water in the flexible tubing attached to one side of the maze might have had an effect on the soil temperature within the maze and thus root orientation (see details in Electronic Supplementary Material).

Experiment 2: root responses to recorded acoustic cues

The aim here was to establish whether roots selectively respond to the sound of water. In test scenario 3 (TS3; Table 1, n = 10) we used playback experiments to first test the directional behavior of roots responding to the recorded sound of water running through a pipe [(WTR-REC); see Fig. S1 for details]. In test scenario 4 (TS4), we examined the directional growth behavior of roots in response to a test scenario, where the playback recording treatment was computer-generated white noise [NOISE; n = 10; Table 1]. As above, both test scenarios involved the acoustic treatment being applied to one side of the maze (A) versus nothing applied to the other side (B), with A assigned to left or right side at random for each maze. In both these test scenarios, seedlings had no direct access to water.

To account for the possible artifact effect caused by the presence of the sound equipment itself, we also tested root responses to two additional control scenarios (TS5, TS6), where either the sound equipment was turned on and broadcasted recorded silence [ZERO Hz; n = 10] or the sound equipment was turned on but not playing [NOT PLAYING; n = 10] (see Table 1; Fig. 2).

All recorded sound treatments were played on continuous loops throughout night and day, using portable MP3 players and small vibration speakers attached directly to the black plastic pot at the base of the maze on the randomly pre-selected side. The acoustic environment in the maze in each treatment was measured using a Digitech QM-1589 sound meter lowered into the soil at the center of the maze. As intended, sound levels in test treatments were 2–3 dB greater than in the ZERO Hz control treatment (105 dB re 1 µPa).

Experiment 3: root responses to co-occurring cues

The aim here was to evaluate the extent to which roots grow in the direction that leads to water using acoustic information when other cues occur simultaneously (as is likely in a natural environment). We further examined the directional behavior of roots in response to three test scenarios (TS7–TS9; Table 1). In all three scenarios, the acoustic treatment applied to one side of the maze (A) consisted of the recorded sound of water [WTR-REC]. Depending on the scenario, another treatment was concomitantly applied to the other side (B). Specifically, in TS7 we used the presence of actual water contained in the transparent plastic tray at the base of the maze [WTR] to test how roots use sensory information delivered by moisture and acoustic gradients to seek water when both are available. In TS8, we applied the computer-generated white noise [NOISE] to test whether roots could discriminate between different sounds. And finally in TS9, we used the sound equipment broadcasting no sound [ZERO Hz] to test whether other factors that are due to the presence of the sound equipment (e.g., effects of magnetic fields), affect or interfere with the directional responses of roots to sound. As above, A and B were assigned to left or right side at random for each maze.

Data analysis

As an initial test of overall significance, we used a proportion test to evaluate whether there were any overall differences among the nine test scenarios in terms of their A vs B proportions. We used a second proportion test to evaluate whether the results were significantly different to random [p(A) = p(B) = 0.5]. To fully analyze and differentiate the various effects acting and interacting in each test scenarios, we also conducted a detailed integrated analysis across all nine test scenarios considered in the present study (see details in electronic supplementary material). We fitted binomial generalized linear models with no intercept terms to predict the probability that root growth direction was to the A treatment side, with model terms to represent each potential effect hypothesized to be influencing the direction of growth. The potential effects considered were an effect of water contact [water contact]; an effect of real water being present with or without contact [water present]; a specific effect of the sound of water [water sound]; a specific effect of recorded white noise [white noise]; an effect of the sound equipment being present [equip], and an additional effect of the sound equipment playing [equip playing]. In each scenario, each effect was hypothesized to be acting towards or against growth to the A side, or to have no influence because the effect was not present at all in that scenario or because it was present equally on both sides (see Table 1 for full details of which effect was assumed to be acting on which side in each test scenario). A model with all these effects was fitted, and then simplified using standard step-wise selection based on Akaike Information Criteria (AIC) values to ensure that only useful predictors were retained in the final model. We also considered a general effect of a continuous sound [sound] (Table 1), but as the sound effect is the sum of the white noise and water sounds effects, it was not possible to test these three effects independently. Therefore, we just tested the effect of replacing [water sound] and [white noise] by [sound] in the model. All analyses were conducted in the R software environment. For the sake of reproducibility, the R script used for analysis is provided in the online supplementary material.

Results

Experiment 1: root directional growth towards the sound of water

In the baseline TS1, an actual water gradient was produced by the presence of 100 ml of water contained in the transparent plastic tray at the base of one side of the maze (see Table 1; Fig. 2a). In this scenario, 8 out of 10 (8/10) seedlings directed their root to the side of the maze where the water source was located (Fig. 4). Seedlings in the TS2 were equally successful (8/10) at locating the water source even though these seedlings had no direct access to water but only to the live sound of water running inside a sealed pipe (Fig. 4). The presence of circulating water in the pipe had no effect on soil temperature (see Electronic Supplementary Material for detailed results on temperature).
Fig. 4

Number of seedlings that directed their roots towards the treatment side A of the maze (white bars) across all test scenarios (TS1–TS9; defined in Table 1). The grey bars indicate seedlings that did not choose the treatment side. The red dotted lines are intended to visually distinguish the scenario specifically tested in each experiment. See also Table S1 (color figure online)

Experiment 2: root responses to recordings of natural and artificial sounds

In TS3 where seedlings were played back the recorded sound of running water, 6/10 of seedlings directed their roots away from, rather than towards, the side of the maze where the sound of water was located (Fig. 4). In TS4 where seedlings were exposed to computer-generated white noise, the observed frequency of seedlings directing their root away from the sound source increased to 8/10 (Fig. 4).

The avoidance behavior observed in TS3 and TS4 was further intensified in TS5 (Fig. 4). In this scenario, 9/10 seedlings directed their roots away from the location of the sound equipment that, albeit being turned on and playing, broadcasted no actual sound. In TS6, when the sound equipment was turned on but not playing [NOT PLAYING], 6/10 seedlings grew away from the equipment (Fig. 4).

Experiment 3: root responses to co-occurring sounds

In TS7, where both moisture and acoustic gradients were available, 2/10 seedlings directed their roots towards the WTR-REC treatment side of the maze, 50% less than in TS3 where the recorded sound of water was the only treatment applied (Fig. 4). The number of seedlings that grew to the WTR side where the water was physically present in TS7 (8/10) was the same as observed in the TS1 scenario where the WTR was the only treatment applied. In the TS8 scenario where WTR-REC and NOISE treatments were co-occurring, the number of seedlings that directed their roots towards the side of the maze with the NOISE (6/10) was threefold more than in the TS4 scenario where the NOISE treatment was applied alone (Fig. 4). However, the number of seedlings growing toward the WTR-REC in this TS8 scenario (4/10) was the same as that observed in the TS3 scenario where the WTR-REC was the only treatment applied (Fig. 4). And lastly, in the TS9 scenario where WTR-REC and ZERO Hz treatments were applied at the same time, the proportion of seedlings growing towards the WTR-REC (7/9) was almost twice that observed in TS3 (4/10) when the treatment was applied alone (Fig. 4). The proportion of seedlings growing toward the ZERO Hz in this scenario (2/9) was also greater than that observed in the TS5 scenario (1/9) where the ZERO Hz was the only treatment applied (Fig. 4).

Integrated analysis of root responses

The proportion tests gave strong support that there were overall differences among scenarios (p = 0.003), and that results were not random (p = 0.002). As expected, treatments had no effect on root L vs R growth direction (p = 0.95) and the L vs R direction did not vary between scenarios (p = 0.91). According to the integrated analysis, the presence of the broadcasting sound equipment had a strongly negative (repulsive) effect on root growth (ΔAIC = 13.5), while contact with water (ΔAIC = 1.7), the sound of water (ΔAIC = 6.9) and white noise (ΔAIC = 1.7) all had positive (attractive) effects on root growth, with the sound of water having the strongest effect (Table 2). The potential hypothesized effects of presence of real water and presence of the sound equipment were not retained in the final simplified model, indicating no evidence that these effects existed (see Table 2 for details of the simplified model). Replacing the terms for water sound and white noise with a single general sound effect did not significantly reduce the explanatory value of the model, indicating that the difference between the two types of sound was not significant (p = 0.91).
Table 2

The final simplified model explaining the observed results in terms of various effects potentially interacting in the different test scenarios, showing for each effect (term retained in the simplified final model), the estimated value for the model coefficient, the associated p value, and the associated estimated probability that the root would grow towards the side of the maze where only that effect is acting, versus a side where no effect is acting

 

Final simplified model

Effect/model term

Coefficient estimate

p value

Probability estimate

Equip on

−2.07

0.00

0.11

Water sound

1.36

0.01

0.80

White noise

1.30

0.06

0.79

Sound

Water contact

1.07

0.07

0.74

Discussion

Our results demonstrate that garden pea seedlings respond to acoustic vibrations generated by water moving inside pipes and propagated through the substrate. Specifically, results from Experiment 1 demonstrate that peas display their typical hydrotropic behavior by growing their roots towards the perceived water source even in the absence of substrate moisture. Thus, it is not necessary for plant roots to have direct access to moisture gradients to sense that water is in the vicinity. Our results not only demonstrate that roots can equally detect and use moisture or acoustic cues to locate water, but also show that roots can be selective about which cue is most advantageous in what circumstance. When both gradients are accessible as in Experiment 3, for example, roots preferentially use moisture in the soil over acoustic vibrations. Given that acoustic vibrations propagate rapidly through soil, conveying real-time information that can be analyzed quickly, sensed at very low intensity and long distances, sound can be an effective cue when a rapid and accurate decision about the most effective direction of growth is required. Accordingly and based on the present findings, we propose that acoustic gradients enable roots to broadly detect a water source at a distance and conceivably, establish the most direct and cost-effective route to that source prior to encountering the associated moisture gradient. Once accessible, the moisture gradient helps the roots to hone in on their target more accurately, hence locating the exact location of the water source.

Unexpectedly, seedlings directed their roots away from the sound of water, when this was a recording played back as in our Experiment 2. As a matter of fact, we found that roots generally grew away from the side of the maze where we positioned the sound equipment, regardless of the broadcasted sound. Moreover, we observed this avoidance response even when silence was played. One possible explanation for these responses is that seedlings were able to detect some other cue emitted by the sound equipment (e.g., magnets in speakers), which affected their root directional growth. Because plants sense and integrate multiple physical parameters for a range of tropic responses including magnetotropism (Galland and Pazur 2005), we considered the possibility that the sound equipment we used in the playback experiments emitted a magnetic field that seedlings sensed and selectively avoided. Hence, we tested the sound equipment and measured a mean magnetic flux density of 3.7 nT ± 0.04 (SE), an intensity that was twice as strong when compared to background readings (i.e., equipment completely switched off; see Fig. S2 and details in Electronic Supplementary Material). While it was beyond the scope of this study to specifically test for magnetosensory abilities of seedlings (see review by Maffei 2015), our findings suggest that the sound equipment we used in the playback experiments likely affected seedlings’ responses to the test acoustic cues by interfering magnetically. Exposure of pea seedlings to very low magnetic fields is known to affect the ultrastructure of root cells by disrupting metabolic systems such as Ca2+ homeostasis (Belyavskaya 2001). Therefore, it is possible that the seedlings sensed the magnetic output of our sound equipment as an environmental condition to be avoided by moving away from its source, even when another sought after cue (e.g., sound of water) is delivered by the same source. This finding is interesting because it demonstrates that seedlings have the ability of “parsing” their sensory world into its components of different types and hence, resolve the influx of information by prioritizing cues that support the overall most beneficial growth decision.

When the strong repulsive effect on root growth associated with the presence of the operating sound equipment was experimentally standardized as in part of Experiment 3 (i.e., where individual peas were exposed to the magnetic disturbance from both sides of the maze) and accounted for in the integrated statistical model, there was some indication that the attractive effect of the recorded sound of water was stronger than that of white noise. This would agree with previous studies, which have demonstrated that plants respond to vibrations in a selective way (Gagliano et al. 2012a; Appel and Cocroft 2014). By showing that Arabidopsis plants were able to discriminate between the vibrations caused by insect feeding and those caused by wind or insect song, for example, Appel and Cocroft (2014) demonstrated that the ability of plants to detect and selectively respond to vibrations has an ecological function. In nature, this selectivity in regards to sounds or vibrations could explain how trees are able to detect different water sources and discriminate between them based on their longer-term availability. For example, streamside trees like scrub oaks and box elders are known to preferentially tap into deeper soil layers for a more reliable source of water rather than the shallow streams, which are more ephemeral (Dawson and Ehleringer 1991).

In our study, the difference between the sound of water and white noise was not great enough to be significant, which may be because the ability of seedlings to grow in the direction that leads to water by using the sound of water as a cue was contingent on the surrounding soundscape. In both cited studies above (Gagliano et al. 2012a; Appel and Cocroft 2014), plants were exposed to a single cue at any given time, thus eliminating the possibility for acoustic interference. In this study, however, seedlings in Experiment 3 were subjected to two acoustic cues at once. It is possible that the ability of seedlings to detect the recorded sound of water was reduced because the cue was obscured by the white noise (i.e., masking effect). Experimental studies in animal systems have demonstrated that the presence of loud white noise can interfere with an individual’s abilities to receive, respond and dispatch acoustic cues and signals. For example, Montgomerie and Weatherhead (1997) showed that foraging success of American robins was reduced when auditory cues were masked by white noise. Bats and squirrels are also affected by white noise and allocate little foraging time to these environments (Schaub et al. 2008). If plant’s abilities to perceive and respond to the surrounding soundscape are also affected by noise, as our findings suggest, what are the ecological ramifications of acoustic pollution on their natural communities? While the urgent need to understand the consequences of altered acoustics on animal populations has been increasingly recognized (Francis and Barber 2013), our findings clearly indicate that the scope of our understanding on the matter needs to be extended to include plants.

A better understanding of the acoustic ecology of plants can also offer insights into new innovative practical applications. For example, our study clearly demonstrated that plants are able to use sound to locate water inside sealed pipes. We propose that some kind of adaptation to other ubiquitous disturbances like those described in Gagliano et al. 2012b (i.e., the resonant acoustic-free oscillations known as the Earth’s “hum”) and Appel and Cocroft 2014 (i.e., feeding sounds of herbivores) which also act via vibrations may have been the key to the root behavior described here. From an evolutionary perspective, the ability to respond to vibrations of various kinds including, for example, those produced by a running stream would have been highly relevant to a broad range of species and beneficial to their survival. We suggest that plants already had the ability to use information of vibrational origin by the time humans started building their first underground pipes, which archeological evidence dates to the Minoan civilization of Crete during the Bronze Age from 3650 to 1400 BC (Wald 2016). Accordingly, plants have had millennia to evolve this ability of responding to environmental vibrations, including the sound of water moving through pipes. This could explain why roots are particularly good at finding and invading sewer pipe systems, even when their pipelines are otherwise sealed and intact. Far from being trivial, root invasion of sewer pipes has severe economic, environmental and social consequences and is a major problem for urban areas around the world. From 2006 to 2013, the Water Corporation in Western Australia spent over AU$ 18 million on sewer pipe blockage repair, of which more than 65% were due to roots intrusion (Xie et al. 2014). In Germany, the costs of root removal and associated pipe repairs are estimated at EUR 28.4 million per year (Östberg et al. 2012). And boasting one of the largest and most complex wastewater systems in the world approaching 7000 km of sewer lines, the city of Los Angeles in the year 1997 spent about US$ 5000 per km to remove roots from its pipes (United States Environmental Protection Agency 1999). As pointed out by Östberg et al. (2012), chemical applications are considered to be the most efficient treatment against root intrusion in pipes, but such an approach can only be applied once the invasion has occurred and importantly, is environmentally hazardous and not sustainable given that its effects are not permanent. Yet, an alternative and environmentally sustainable way to overcome problems associated with root invasions may be as simple as utilizing soundproof materials in the construction of sewer pipelines.

While the mechanisms of how plants detect sounds are still to be identified (although possible mechanisms have been suggested; Gagliano et al. 2012b, c; Gagliano 2013a, b), plant responses to acoustic cues, whether these are vibrations generated by neighboring plants (as suggested by Gagliano et al. 2012b, c), attacking herbivores (Appel and Cocroft 2014), buzz pollinators (Proctor et al. 1996) or human activities (as shown in this study), demonstrate that sound and vibrations play an important ecological role in the life of these organisms. Hypothesis-driven research is required to systematically investigate the capacity of plants to detect and use sounds. The key questions to be addressed are what are the mechanisms underlying the ability of plants to discriminate sound sources and their information content, and how this knowledge can be responsibly applied in a beneficial manner to plants and animals alike. The answers are clearly important to better understand the processes underlying species interactions and co-evolution, including bio-inspired innovative solutions for application to real world problems.

Notes

Acknowledgements

We thank R. Creasy, W. Piasini, H. Etchells, T. Betts, N. Clairs, R. Malkin and P. Tallai for their assistance, and H. Heilmeier and two anonymous reviewers for valuable comments on the manuscript. This work was supported by Research Fellowships from the University of Western Australia and the Australian Research Council (ARC grant n. DE130100018) to MG*.

Author contribution statement

MG* conceived and designed the experiments. MG* and MG performed the experiments and collected data MG*, MD and MR analyzed and interpreted the data. MG* and MR drafted the paper. All authors edited and critically revised the final version, and approved its publication.

Compliance with ethical standards

Conflict of interest

The authors declare no competing interests.

Supplementary material

442_2017_3862_MOESM1_ESM.pdf (915 kb)
Supplementary material 1 (PDF 914 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Centre for Evolutionary Biology, School of Animal BiologyUniversity of Western AustraliaCrawleyAustralia
  2. 2.Australian Institute of Marine ScienceCrawleyAustralia
  3. 3.Oceans InstituteUniversity of Western AustraliaCrawleyAustralia
  4. 4.School of Plant BiologyUniversity of Western AustraliaCrawleyAustralia

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