Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,668
result(s) for
"echolocation"
Sort by:
How dolphins and other animals use sonar
Biosonar is a kind of sonar produced and used by animals such as dolphins. Readers discover the biology behind biosonar and are introduced to other animals that also have this adaptation, including bats and shrews. Photographs of these animals are presented alongside a variety of facts and a graphic organizer. Informative text touches on science curriculum topics, such as animal adaptations, predator-prey relationships, and the science of sound.
Book
Audition and balance: The impact of echolocation on postural control
The links between the perception of acoustic signals and postural control remain largely unexplored, and the control models proposed to date have yet to assign these sensory inputs a role alongside visual, proprioceptive, and vestibular information. Research on this topic has produced varied conclusions, likely due to significant methodological differences. Our study makes novel contributions by aiming to determine the impact of the auditory system-specifically through the perception of echoes-on postural control during quiet standing and to identify the potential link between auditory perception and postural stability. To create controlled acoustic conditions, we developed an automated device representing a reflective object, positioned statically in two locations and dynamically moved in two directions relative to the participants. Eleven naive, blindfolded adult participants (mean age: 23 years ±2.2; mean height: 174 cm ±7.4) with normal hearing underwent kinematic (optoelectronic cameras) and kinetic (force plate) analyses in a semi-anechoic room. After each trial, participants reported their perception of the object's presence, position, movement direction, and distance. Results demonstrated improved object perception and greater stability in dynamic conditions, particularly when the target moved towards the participants, compared to static conditions. However, no significant correlation between perception and postural stability was observed. Our findings suggest that acoustic information, especially through echo perception, could plays a role in postural control processes alongside visual, proprioceptive, and vestibular input. However, more controlled studies are necessary to examine the relationship between perception and postural stability.
Journal Article
Bats
Bats, the only flying mammals, comprise almost 25% of mammalian species. They are excellent navigators, highly social, and extremely long-lived. Their sense of echolocation has been studied for many years — but many species possess also excellent vision and olfaction. In recent years, bats have emerged as new models for neurobiology of navigation, social neuroscience, aging, and immunity.
Journal Article
Detecting newly installed bat boxes: Bats’ prior familiarity with artificial roosts may play a bigger role than improved echo-reflective properties
Habitat loss in Europe severely affects bats, particularly tree-roosting species, due to the decreasing availability of tree cavities. One common conservation strategy is the installation of artificial roost boxes. However, the occupation of newly installed roost boxes can take up to several years, and the underlying mechanisms for successful roost detection in bats are still poorly understood. This study proposes enhancing the detectability of roost boxes to echolocating bats by incorporating hollow hemispheres that provide highly conspicuous echoes. The hemispheres strongly reflect the echolocation calls of passing bats and are thus well detectable over a broad range of angles. We hypothesized that roost boxes equipped with these hemispheres would attract more bats and exhibit greater bat activity than standard, unmodified boxes. To evaluate this, we placed 30 modified boxes and 30 unmodified boxes across three forest areas in Northern Germany, each differing in proximity to known bat hibernation sites and the prior presence of artificial roosts. We monitored bat activity by measuring light beam interruptions at each box and found that the activity of bats at the boxes varied considerably. Our findings indicate that, contrary to our hypothesis, bat activity was more strongly influenced by their prior experience with artificial roosts than by the increased detectability provided by hollow hemispheres. Furthermore, our study revealed that light beam interruptions indicated bat presence at the boxes earlier than visual checks for bats or feces, showcasing the benefits of non-invasive monitoring techniques. Conservation efforts are complex, and these results imply that for effective bat conservation, increasing bats’ familiarity with artificial roosts may be more important than merely enhancing the detectability of these structures.
Journal Article
Advancing bat monitoring: Assessing the impact of unmanned aerial systems on bat activity
With the increasing height and rotor diameter of wind turbines, bat activity monitoring within the risk area becomes more challenging. This study investigates the impact of Unmanned Aerial Systems (UAS) on bat activity and explores acoustic bat detection via UAS as a new data collection method in the vicinity of wind turbines. We tested two types of UAS, a multicopter and a Lighter Than Air (LTA) UAS, to understand how they may affect acoustically recorded and analyzed bat activity level for three echolocation groups: Pipistrelloid, Myotini, and Nyctaloid. We hypothesized (i) that the LTA UAS will not affect bat activity levels while a multicopter, due to higher noise emission, might have a negative impact. Our results support this hypothesis, because multicopter flights have a highly significant negative impact on bat activity levels with a medium effect size, particularly for the Myotini ( P < 0.001, d m = 0.54) and Nyctaloid group ( P < 0.001, d n = 0.55) and a small effect size for the Pipistrelloid group ( P < 0.001, d p = 0.36). In contrast, the LTA UAS had no significant effect on bat activity for each echolocation group ( P > 0.05 for each group), suggesting its suitability for non-intrusive acoustic monitoring. Furthermore, we hypothesized (ii) that larger UAS propellers prevent the deterrent effect on bats. However, despite the use of larger propellers for the multicopter UAS compared to previous studies, we observed a deterrence effect for all echolocation groups. Additionally, we hypothesized that (iii) any initial deterrence or attraction effect might decrease over time. Our results did not support this hypothesis because we did not observe any habituation of bats to UAS within the 15-minute flight period. Our study highlights the potential of UAS for bat monitoring but underscores the critical importance of selecting appropriate UAS types and operating noise levels for successful surveillance efforts.
Journal Article
Automated echolocation classifiers vary in accuracy for northeastern U.S. bat species
Acoustic surveys of bat echolocation calls are an important management tool for determining presence and probable absence of threatened and endangered bat species. In the northeastern United States, software programs such as Bat Call Identification (BCID), Kaleidoscope Pro (KPro), and Sonobat can automatically classify ultrasonic detector sound files, yet the programs’ accuracy in correctly classifying calls to species has not been independently assessed. We used 1,500 full-spectrum reference calls with known identities for nine northeastern United States bat species to test the accuracy of these programs using calculations of Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity (SN), Specificity (SP), Overall Accuracy, and No Information Rate. We found that BCID performed less accurately than other programs, likely because it only operates on zero-crossing data and may be less accurate for recordings converted from full-spectrum to zero-crossing. NPV and SP values were high across all species categories for SonoBat and KPro, indicating these programs’ success at avoiding false positives. However, PPV and SN values were relatively low, particularly for individual Myotis species, indicating these programs are prone to false negatives. SonoBat and KPro performed better when distinguishing Myotis species from non- Myotis species. We expect less accuracy from these programs for acoustic recordings collected under normal working conditions, and caution that a bat acoustic expert should verify automatically classified files when making species-specific regulatory or conservation decisions.
Journal Article
Effectiveness of different sounds in human echolocation in live tests
Echolocation is a vital method of spatial orientation for many visually impaired individuals who are willing to and able to learn it. Blind echolocators use a variety of sounds, such as mouth clicks, cane taps, or specialized sound-emitting devices, to perceive their surroundings. In our study, we examined the effectiveness of several different sounds used in echolocation by conducting trials with 12 blind and 14 sighted volunteers. None of the participants had received formal training in echolocation, though a number identified as self-taught experts. The sounds tested included those played from a loudspeaker, generated by a mechanical clicker, or made by the participants themselves. The task given to the participants was to identify the direction and distance to an obstacle measuring 1x2 meters in an outdoor environment, with the obstacle placed in one of nine possible positions. Our findings indicated that the blind participants displayed significantly better echolocation skills when compared to the sighted participants. The results of the blind participants were also strongly divided into two distinct subgroups—totally blind participants performed much better than those which were legally blind, but had some residual vision. In terms of sound comparisons, we found that sounds with a center frequency near 3-4kHz and a wide spectrum provided higher accuracy rates than those with lower frequency peaks. Sighted participants performed best with 3kHz and 4kHz percussion sounds, while the blind group performed best with blue and pink noise. The loudspeaker generated tones generally yielded better results than those generated by the participant (using a mechanical clicker, mouth clicks or hand claps). These results may be useful in developing training programs that teach echolocation as well as artificial sounds to improve echolocation effectiveness.
Journal Article
Automatic detection and annotation of eastern Caribbean sperm whale codas
A key technology for sperm whale (
Physeter macrocephalus
) monitoring is the identification of sperm whale communication signals, known as
codas
. In this paper we present the first automatic coda detector and annotator. The main innovation in our detector is graph-based clustering, which utilizes the expected similarity between the clicks that make up the coda. Results show detection and accurate annotation at low signal-to-noise ratios, separation between codas and echolocation clicks, and discrimination between codas from simultaneously emitting whales. Using this automatic annotator, insights into the characterization of sperm whale communication are presented. The results include new types of coda signals, analysis of the distribution of coda types among different whales and for different years, and evidence for synchronization between communicating whales in terms of coda type and coda transmission time. These results indicate a high degree of complexity in the communication system of this cetacean species. Source code and data for our system is publicly available
Journal Article
Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data
Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.
Journal Article
Buzzfindr: Automating the detection of feeding buzzes in bat echolocation recordings
Quantification of bat communities and habitat heavily rely on non-invasive acoustic bat surveys the scope of which has greatly amplified with advances in remote monitoring technologies. Despite the unprecedented amount of acoustic data being collected, analysis of these data is often limited to simple species classification which provides little information on habitat function. Feeding buzzes, the rapid sequences of echolocation pulses emitted by bats during the terminal phase of prey capture, have historically been used to evaluate foraging habitat quality. Automated identification of feeding buzzes in recordings could benefit conservation by helping identify critical foraging habitat. I tested if detection of feeding buzzes in recordings could be automated with bat recordings from Ontario, Canada. Data were obtained using three different recording devices. The signal detection method involved sequentially scanning narrow frequency bands with the “Bioacoustics” R package signal detection algorithm, and extracting temporal and signal strength parameters from detections. Buzzes were best characterized by the standard deviation of the time between consecutive pulses, the average pulse duration, and the average pulse signal-to-noise ratio. Classification accuracy was highest with artificial neural networks and random forest algorithms. I compared each model’s receiver operating characteristic curves and random forest provided better control over the false-positive rate so it was retained as the final model. When tested on a new dataset, buzzfindr’s overall accuracy was 93.4% (95% CI: 91.5%– 94.9%). Overall accuracy was not affected by recording device type or species frequency group. Automated detection of feeding buzzes will facilitate their integration in the analytical workflow of acoustic bat studies to improve inferences on habitat use and quality.
Journal Article