Analysis Of Echolocation Calls Research Articles

Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment

Bats have an important role in the ecosystem, and therefore an effective detection of their prevalence can contribute to their conservation. At present, the most commonly methodology used in the study of bats is the analysis of echolocation calls. However, many other ultrasound signals can be simultaneously recorded, and this makes species location and identification a long and difficult task. This field of research could be greatly improved through the use of bioacoustics which provide a more accurate automated detection, identification and count of the wildlife of a particular area. We have analyzed the calls of two bat species—Pipistrellus pipistrellus and Pipistrellus pygmaeus—both of which are common types of bats frequently found in the Iberian Peninsula. These two cryptic species are difficult to identify by their morphological features, but are more easily identified by their echolocation calls. The real-life audio files have been obtained by an Echo Meter Touch Pro 1 bat detector. Time-expanded recordings of calls were first classified manually by means of their frequency, duration and interpulse interval. In this paper, we first detail the creation of a dataset with three classes, which are the two bat species but also the silent intervals. This dataset can be useful to work in mixed species environment. Afterwards, two automatic bat detection and identification machine learning approaches are described, in a laboratory environment, which represent the previous step to real-life in an urban scenario. The priority in that approaches design is the identification using short window analysis in order to detect each bat pulse. However, given that we are concerned with the risks of automatic identification, the main aim of the project is to accelerate the manual ID process for the specialists in the field. The dataset provided will help researchers develop automatic recognition systems for a more accurate identification of the bat species in a laboratory environment, and in a near future, in an urban environment, where those two bat species are common.

Bats are still not birds in the digital era: echolocation call variation and why it matters for bat species identification

The recording and analysis of echolocation calls are fundamental methods used to study bat distribution, ecology, and behavior. However, the goal of identifying bats in flight from their echolocation calls is not always possible. Unlike bird songs, bat calls show large variation that often makes identification challenging. The problem has not been fully overcome by modern digital-based hardware and software for bat call recording and analysis. Besides providing fundamental insights into bat physiology, ecology, and behavior, a better understanding of call variation is therefore crucial to best recognize limits and perspectives of call classification. We provide a comprehensive overview of sources of interspecific and intraspecific echolocation call variations, illustrating its adaptive significance and highlighting gaps in knowledge. We remark that further research is needed to better comprehend call variation and control for it more effectively in sound analysis. Despite the state-of-art technology in this field, combining acoustic surveys with capture and roost search, as well as limiting identification to species with distinctive calls, still represent the safest way of conducting bat surveys.

Multiscale, presence‐only habitat suitability models: fine‐resolution maps for eight bat species

Summary To manage anthropogenic environmental change for the benefit of biodiversity, we must improve our understanding of the complex relationships between organisms and their environment. We have developed multiscale habitat suitability models (HSMs) for bats, a mobile group of mammals, for a geographically varied region of the UK. We ask whether the models have sufficient accuracy to contribute to informed decision‐making in habitat management and in minimizing the impact of climate change and human infrastructural development. We used acoustic surveys supplemented by catching to gather presence data for eight species from 30 sites across the south of the Lake District National Park in NW England. Species were identified by manual and automated extraction and analysis of echolocation calls. Fine‐resolution (50 and 100 m) habitat maps were generated at twelve spatial scales by calculating the variables across squares of increasing size, from 100 to 6000 m, around each focal 50 or 100 m square. Presence‐only HSM software, MaxEnt, was used to determine the predictive power of each habitat variable at each scale. Multiscale models included data for each variable at the scale at which it had the strongest relationship with the presence of each species. The best multiscale models were selected using fivefold cross‐validation, with backwards, stepwise variable removal, whilst minimizing residual spatial autocorrelation and sampling bias. Further tests with independent field data indicated good model transferability across the entire National Park. Foraging bats were generally most strongly associated with variables measured at small spatial scales and distance measures. However, each species responded differently across the range of scales, and strong associations were also found at the largest scale of analysis (6000 m). Synthesis and applications. The best models for determining habitat suitability had few variables, making them easy to interpret and use in practical conservation planning. The approach is applicable to any taxa for which reliable presence records are available, providing insight into the potential impacts of land‐use and environmental change. Maps identify areas of conservation concern, such as hot spots for diversity, rare or vulnerable species and potential or threatened network corridors, making them useful for ecological impact assessment of proposed developments, and to conservation managers planning habitat creation or improvement.

Triggering bat detectors: automatic vs. manual mode

Ultrasound detectors have revolutionized the study of bats, as they allow for cost-effective gathering of large amounts of data on bat activity. The identification of bat species through the analysis of echolocation calls is possible, as long as the researcher is well aware of the features and limitations of the detector and methods used. In bat studies, field logistics frequently leads to the need of leaving the ultrasound detector in the study area for a given number of nights, set to an automatic triggering mode. Nonetheless, the reliability of this option, in com- parison with the manual mode, is still uncertain, even for commonly used bat detector models. In this study, we assessed the efficacy of a popular time-expansion bat detector model set to the automatic trigger mode, when compared with the manual mode. We sampled bat activity in five different habitats for 23 nights with two bat detec- tors operating simultaneously, side by side, one set to manual mode and the other set to automatic mode. Our results showed significant differences in detected species richness, species diversity, and bat activity between the manual and the automatic modes. We present practical information and clarify the adequacy of using the ana- lyzed bat detector in automatic conditions.

Echolocation Calls of Bats from Madeira Island: Acoustic Characterization and Implications for Surveys

Acoustic surveys represent a powerful tool to assess bat distribution and habitat preferences and are widely applicable to monitoring and conservation schemes. However, their correct application requires the development of robust and reliable identification procedures. Little information is available on the bats of Madeira Island (Portugal), particularly their ecological requirements, distribution and population trends, and proper guidelines for their monitoring and conservation have yet to be defined. In this study, we present the first analysis of echolocation calls from species occurring on the island to provide a tool for bat identification during acoustic surveys. Seven hundred and ninety one time-expanded recordings of search phase echolocation calls in cluttered and uncluttered habitats of the Macaronesia endemic pipistrelle Pipistrellus maderensis, Madeira Leisler's bat Nyctalus leisleri verrucosus and grey long-eared bat Plecotus austriacus were analysed and an acoustic repertoire with their temp...

Haracteristics and Analysis of Echolocation Calls by <I>Coelops frithi</I>

The echolocation calls of Coelops frithi were recorded during free flight using an ultrasonic detector U30, and were analyzed using the software Batsound 3.0. Our results showed that C. frithi emitted high frequency (145.4±10.9 kHz), broad bandwidth (62.6±9.2 kHz), and short (1.67±0.4 ms) frequency-modulated (FM) calls with 2 harmonics, which suggested polymorphic echolocation calls in Hipposideridae because other hipposiderid species emitted constant frequency (CF) calls. The analysis of the cranial measurements of C. frithi indicated that it was a nasal emitter like other hipposiderids, which suggested the single emitting form in this family. We considered that C. frithi emitted FM calls mainly due to the selection pressure of evolution and the adaptability between wing morphology and echolocation calls.

Data, Sample Sizes and Statistics Affect the Recognition of Species of Bats by Their Echolocation Calls

Identification of bat species based on analysis of echolocation calls can be affected by the way data are manipulated, the diversity of species, and call variability. We document the effects of sample sizes and a priori assignment of calls by species on the outcome of discriminant function analysis (DFA) and multinomial logistic regression (MLR) of features of echolocation calls, and determine which features of calls are most useful for identification. We used recorded echolocation calls of eight species readily distinguishable by call features, including molossids, emballonurids and a moormopid recorded at sites in Belize, Brazil, and Mexico. On individual calls, we measured four features: frequency with most energy, highest and lowest frequencies and call durations obtained from sequences consisting of 10 calls. Cluster analysis and multiple analyses of variance indicated significant differences between the calls of different species. Outcomes of DFA and MLR were affected by both sample sizes (numbers of calls, numbers of sequences) and the subjective approach that researchers take to their data (i.e., categorizing calls or sequences of calls by species). Levels of variation in calls of some species in our sample often precluded the use of single calls in making call-based identifications. Accurate documentation of variability in echolocation behavior of sympatric bats is a prerequisite for an effective sound-based bat survey.

Acoustic identification of eight species of bat (mammalia: chiroptera) inhabiting forests of southern hokkaido, Japan: potential for conservation monitoring.

Assessing the impact of forest management on bat communities requires a reliable method for measuring patterns of habitat use by individual species. A measure of activity can be obtained by monitoring echolocation calls, but identification of species is not always straightforward. We assess the feasibility of using analysis of time-expanded echolocation calls to identify free-flying bats in the Tomakomai Experimental Forest of Hokkaido University, Hokkaido, northern Japan. Echolocation calls of eight bat species were recorded in one or more of three conditions: from hand-released individuals, from bats flying in a confined space and from bats emerging from their roost. Sonograms of 171 calls from 8 bat species were analyzed. These calls could be categorized into 3 types according to their structure: FM/CF/FM type (Rhinolophus ferrumequinum), FM types (Murina leucogaster, Murina ussuriensis, Myotis macrodactylus and Myotis ikonnikovi) and FM/QCF types (Eptesicus nilssonii, Vespertilio superans and Nyctalus aviator). Sonograms of the calls of R. ferrumequinum could easily be distinguished from those of all other species by eye. For the remaining calls, seven parameters (measures of frequency, duration and inter-call interval) were examined using discriminant function analysis, and 92% of calls were correctly classified to species. For each species, at least 80% of calls were correctly classified. We conclude that analysis of echolocation calls is a viable method for distinguishing between species of bats in the Tomakomai Experimental Forest, and that this approach could be applied to examine species differences in patterns of habitat-use within the forest.

Sympatric distribution of two cryptic bat species across Europe

The analysis of echolocation calls and mitochondrial DNA sequences recently revealed the existence of two cryptic bat species in Western Europe which were regarded as «Pipistrellus pipistrellus» for more than 200 years. We now present data on acoustic and genetic characters across Europe including a novel genetic marker from the nuclear genome. Intraspecific variation of end frequencies of echolocation calls did not differ between geographic regions and only a little overlap existed between the two species. Nuclear and mitochondrial DNA sequences for the two species were highly divergent. No evidence was found for additional cryptic species in this group. With the exception of Scandinavia, both species occur across the whole of Europe and largely overlap in their range. The distribution of the species using echolocation calls at higher frequencies extends further north and the same species is also more common in the south along the Mediterranean Sea.

Sympatric distribution of two cryptic bat species across Europe

The analysis of echolocation calls and mitochondrial DNA sequences recently revealed the existence of two cryptic bat species in Western Europe which were regarded as «Pipistrellus pipistrellus» for more than 200 years. We now present data on acoustic and genetic characters across Europe including a novel genetic marker from the nuclear genome. Intraspecific variation of end frequencies of echolocation calls did not differ between geographic regions and only a little overlap existed between the two species. Nuclear and mitochondrial DNA sequences for the two species were highly divergent. No evidence was found for additional cryptic species in this group. With the exception of Scandinavia, both species occur across the whole of Europe and largely overlap in their range. The distribution of the species using echolocation calls at higher frequencies extends further north and the same species is also more common in the south along the Mediterranean Sea.

Identification of New Zealand bats (Chalinolobus tuberculatus and Mystacina tuberculata) in flight from analysis of echolocation calls by artificial neural networks

AbstractTime‐expanded and heterodyned echolocation calls of the New Zealand long‐tailed Chalinolobus tuberculatus and lesser short‐tailed bat Mystacina tuberculata were recorded and digitally analysed. Temporal and spectral parameters were measured from time‐expanded calls and power spectra generated for both time‐expanded and heterodyned calls. Artificial neural networks were trained to classify the calls of both species using temporal and spectral parameters and power spectra as input data. Networks were then tested using data not previously seen. Calls could be unambiguously identified using parameters and power spectra from time‐expanded calls. A neural network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 40 kHz (the frequency with the most energy of the fundamental of C. tuberculatus call), could identify 99% and 84% of calls of C. tuberculatus and M. tuberculata, respectively. A second network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 27 kHz (the frequency with the most energy of the fundamental of M. tuberculata call), could identify 34% and 100% of calls of C. tuberculatus and M. tuberculata, respectively. This study represents the first use of neural networks for the identification of bats from their echolocation calls. It is also the first study to use power spectra of time‐expanded and heterodyned calls for identification of chiropteran species. The ability of neural networks to identify bats from their echolocation calls is discussed, as is the ecology of both species in relation to the design of their echolocation calls.