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Thus, we find that although bandwidth is not a critical parameter for classification in our current algorithm, it is nevertheless better to record for longer, to collect more data that could potentially be used to strengthen confidence in classification using other algorithms that take this into account. We have developed a classification algorithm based on Maximum Likelihood Estimation, to identify the most probable species that could have produced the observed frequencies in a recording, given prior knowledge of the characteristic frequency distributions for each species.

Although acoustic distinctions were very clear between Culex and Anopheles mosquitoes see aforementioned paragraph , we did not have other means to distinguish them in the field according to species at the time of our study.

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Because of permit-related limitations in transporting mosquitoes, most samples could not be brought back to our lab. We expanded our analysis of wild caught mosquitoes to survey wingbeat frequencies for more than 80 individual recordings of Aedes sierrensis Figure 4—figure supplement 2. Although the inter-quartile range varies and does not overlap for a few individuals, the absolute magnitude of this difference order of a few tens of Hz is relatively small compared to the spread of around Hz for a given species distribution.

However, we cannot make direct comparisons between the effects of inter-species and intra-species variations on classification, as these are independent contributions to classification errors. Differentiation or classification is an inherently probabilistic operation. Hence the strongest statement we can make is only in terms of the chance of classifying a recording from a given species correctly. It is not necessary that distinct species should have frequency distributions very different from each other.

Indeed, this is not the case Figure 3A , with some species pairs having highly similar frequency distributions.

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It is quite possible that the differences between some individuals of one species will be greater than or equal to the differences between that species and another. In the cases of a few species with high overlap, correct classification of individual recordings is also very challenging, with a high probability of making mistakes.

Conversely, it is also possible that there are some outliers within a species, which are consistently misclassified as another. These are extreme cases, and in such situations metadata or acoustic parameters other than wingbeat frequency need to be brought into the picture, to create a distinction between species. There is also much scope for developing Bayesian approaches to solve this problem, to continuously refine classification accuracy based on prior observations and knowledge; however that lies outside the scope of this first manuscript.

Chen et al. Insect Behav. Yet, we agree with the reviewers that time cannot create an absolute distinction between the two species. Still, we point out that it nevertheless presents opportunities for partially improving classification between the otherwise difficult-to-distinguish pair of Aedes aegypti and Anopheles gambiae. We expect that as we collect more field data to build a more complete and fine-grained picture of the activity of these two species over time, it may become possible to build a suitable filter to differentiate the two based on this data.

Here, we clarify that only a single colony of Anopheles quadrimaculatus was studied, but it has been presented adjacent to the similarly named Anopheles quadriannulatus — also a single colony. However, in the specific cases of Aedes aegypti, Anopheles gambiae , and Anopheles arabiensis , we had recorded multiple colonies, and Figure 3A represents the aggregate distribution from all colonies grouped together.

Here, we discuss the similarity between colonies for An. However, we also note that frequency distributions vary significantly for An. While two of the colonies presented for Ae.

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We discuss the implications of this, indicating that while some species may be classified using universal frequency databases, others may need local distinctions to be made. We disagree that the potential for citizen science is largely diminished if multiple calibration datasets are required. We point out that acoustic records do not even exist for most species, and out work presents the first systematic groundwork done to survey a greater number of species across different areas.

Our proposed tool is accessible to the general public, and calls for a large number of users, making local distributions possible. Acoustic wingbeat data also provides a quantitative framework for standardized data collection across many studies, because of which any number of decentralized local surveys can be stitched together to create an increasingly detailed picture of mosquito populations and their associated sounds in different locations.

More people engaging in data collection also improves the overall performance of the system. For example, local vector control districts or field entomologists at any location can create annotated databases relevant to their location, using the same framework and approach. With an increasing number of such local databases, it may become possible to study these variations within species on a much larger scale than hitherto possible. We lay the basis for such an effort in our current manuscript, with the outlook that much larger datasets will be collected via community efforts in the future.

We recognize the importance of presenting quantitative classification approaches in our work as the primary concern of the reviewers. Our approach has been briefly described in our second response to point A. In brief, we have chosen a Maximum Likelihood Estimation approach to classify recordings from female mosquitoes of the 20 different species we survey in our work.

Here, we note that prior work that describes classification algorithms typically focus on differentiating a smaller number of classes between 2 to 8. This even includes males of the same species considered as distinct classes, which have inherently divergent frequencies as compared to females and are highly unlikely to be misclassified as such. Thus, our current algorithm demonstrates classification among a greater number of species than has been achieved so far.

We detail the performance of this algorithm on various metrics, including performance on bootstrapped validation data, performance on new test data, improvement of classification with the inclusion of location metadata, and dependence of classification accuracy on the duration of the recording. In our current work, we have used a Maximum Likelihood Estimation MLE approach to classification, as discussed in our second response to point A and our first response to point B.

We used this approach as it is easy to implement and shows good classification accuracies, while being highly appropriate for the nature of the problem we are solving.


There is some existing literature that focuses on developing algorithms for the automated identification of insect species using wingbeat frequencies Moore et al. The scope of this current manuscript is to establish a new method to bring data-collection out of the lab and into hands of common users in the field. In continuing work ongoing in this area, we have already begun pursuing different machine learning approaches to acoustic species classification, based on identifying new features that characterize mosquito sounds, or developing new techniques to classify spectrograms.

The MLE algorithm presented here sets a benchmark for future algorithms to match or outperform. We would like to clarify that our initial analysis did not explicitly mean to discard higher harmonics or disregard other acoustic properties of the mosquito signal, such as number of harmonics, degree of harmonicity, spread across a trace, etc.

As we had initially not explicitly discussed classification algorithms, we instead highlighted fundamental frequency as the most common identifier used in the literature to classify mosquitoes, widely presented in other works dealing with insect identification Reed et al. Genetics ; Sotavalta Acta Entomol. We are currently working on new algorithms and analysis of user generated mosquito acoustic data in this regard, to identify such features using data mining methods and incorporate them into future classification algorithms, particularly as we explore more and more mosquito species.

Collecting field data from a wide group of people and instruments is the key to identify robust predictors as we expand our test data sets. However, we would like to point out here that we have also chosen to specifically exclude the relative amplitude for harmonics at different frequencies as a potential identifier. Three factors related to the recording techniques we use — mobile phone microphone frequency response, possible confinement in cups or bottles, and field recording against variable background noise — can sometimes alter the ratio of amplitudes for the first two harmonics.

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  6. This is particularly evident in these cases:. For mosquitoes with low frequency wingbeats, some phones like the Xperia Z3 Compact tend to amplify the first overtone as opposed to the fundamental frequency — an effect that can be observed in Figure 1—figure supplement 2.

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    We have provided a spectrogram giving an example of this in Author response image 1 , for two Aedes sierrensis mosquitoes recorded in the field while confined in 50mL Falcon tubes. Author response image 2 shows a situation where the second overtone is most easily distinguishable, and our processing algorithm tries to extrapolate fundamental frequency based on detecting the second overtone. Since our method should be able to deal with data from a variety of species, collected on many different phone models having slightly varying frequency response curves, in highly variable field environments, we cannot consider a feature that is selectively distorted in some potentially unknown subset of the data.

    We also present analyses of our classification accuracy with and without a location filter applied to this data. The limitations of mobile phone hardware enforce requirements on a minimum range and specific orientation when recording mosquitoes. We include supplementary movies that explicitly provide a visual demonstration of recording for readers.

    Mobile phone microphones are inherently short-range sensors, as they are typically optimized to pick up sound from a person speaking close to the mouthpiece. This also implies that mobile phones will only be able to pick up sounds from mosquitoes within this range, and cannot passively acquire signals from far away mosquitoes without being overwhelmed by noise. For this reason, our recording methods require active human involvement in bringing the phone sufficiently close to the mosquito.

    For the effects of orientation, we consider two conditions — the orientation of the insect in flight with respect to the microphone which is not under the control of the user , and the orientation of the phone that directs the mosquito towards or away from the insect which is completely under the control of the user.

    The amplitude of the wingbeat sound has a directional variation, being about 10dB louder ahead of and behind the mosquito. However, this is not a significant factor affecting our data, since we make recordings in free flight where both the orientation and distance of the insect with respect to the mobile phone is highly variable during flight sequences. For the typical amplitudes of dB that we record for mosquito sound as measured in the lab on free-flying caged mosquitoes , 10dB does not reduce the sound below detectable levels.

    For the second aspect, the orientation of the phone itself is critical to improving the signal-to-noise ratio of the recording, much like the range. It is important that the user orient the primary microphone towards the mosquito as directly as possible, as the sounds that are not in the line of sight of the primary mic are generally considered noise by the phone dual microphone noise reduction technology.

    The general location of these microphones is commonly known to users, and we also describe how to look for them in our instruction sheets.