<p>Choosing duty cycles is a sort of sampling.
Therefore you should choose your duty cycle such that you do not undersample the feature you would like to study. I would suggest to have at least one or two samples over the period of feature.</p>
<p>Example: if the beaked whales echolocate for 30 minutes every 2 hours, then you should have one sampling period every 20 minutes (&lt; 30 min), better every 15 min.</p>

CC BY-SA 4.0
2022-07-12T05:54:27.673Z
2022-07-12T05:54:27.673Z
WMXZ
<p>One important factor to consider is simply the likely presence of the signal of interest across a 24-hour period. For example, if you are targeting a nocturnal species, you may wish to start your recordings 30 minutes before sunset and end 30 minutes after sunrise, avoiding significant periods of likely-empty data.</p>

CC BY-SA 4.0
2022-07-12T20:38:49.390Z
2022-07-12T20:38:49.390Z
dtsavage
<p>This is a really interesting question and there probably isn't a 'perfect' duty cycle.</p>
<p>The main two things to consider are</p>
<ol>
<li><p>How much continuous data do you need to make a robust decision about the presence of a particular species.</p>
</li>
<li><p>How similar (auto-correlated) are adjacent chunks of time likely to be.</p>
</li>
</ol>
<p>So for a species which has long duration calls which may not occur very often, you may want quite a long recording period. For example, blue whale calls can be up to 18s long, and intervals between calls my be over a minute, so you'd want individual recordings to be many minutes long if you require several calls to make a robust decision. However, blue whales are detectable at long ranges, so if you can hear a blue whale in one 10 minute time slot, it's also likely you'll hear it in the next which doesn't really add much data (auto-correlation), so why not record 10 minutes every half hour ?</p>
<p>For a species which produces many calls per second (e.g. porpoises, bats), but may only be in range for a few seconds as they pass the recorder, a much shorter duty cycle would probably be more appropriate so as to reduce the chances of missing passing animals completely.</p>
<p>This last point does of course raise the question of the statistical analysis you intend to use with your data once it's collected. With a long duty cycle, you might argue that you have periods when you detect everything and periods when you detect nothing, so the duty cycle becomes little more than a simple scaling factor. With very short duty cycles, this may not be so clear cut since you'll have to more carefully consider animal movement speed and detection range and think about what the chances are of missing animals completely. This may be an argument for longer cycles, accepting that you will miss animals, but the statistical analysis may be simpler.</p>
<p>Final things to consider may be the sheer number of files you'll generate with short duty cycles, the time it takes the recording device to start up, and possibly also how long any automatic detectors you're using may take to 'settle' (i.e. get a reasonable measure of background noise to set detection thresholds against) before they work as advertised.</p>

CC BY-SA 4.0
2022-07-13T11:03:34.793Z
Doug makes many excellent points.  To address many of these, to optimise sampling and to understand its statistical consequences, it will be very useful to have some continuous, completely analysed data sets which you could then sample using different protocols.
Jonathan C Gordon
CC BY-SA 4.0
2022-07-12T12:32:19.443Z
2022-07-12T12:32:19.443Z
Doug Gillespie
<p>Acoustic monitoring is limited by available power and memory storage, but duty cycling recordings can extend the time period that is monitored. I understand that choosing an apporpriate duty cycle will depend on your species and research question, but want to make sure I'm making the best decision. For example,</p>
<p><a href="https://asa.scitation.org/doi/full/10.1121/10.0009752?casa_token=fPpzNpwnJmgAAAAA%3A3sRfakdhPowKj76e9wxTqiP9WceCn8teV8b0_rJFT7TjAjJ8cTmnC7F7Pq_lz52pnV03TbMqXJte" rel="noreferrer">Rand et al 2020</a> found that their ability to detect killer whale ecotypes and event durations was impacted by different duty cycles, but there was little impact on their ability to detect daily presence.</p>
<p>For beaked whales, <a href="https://asa.scitation.org/doi/full/10.1121/1.4955009" rel="noreferrer">Stanistreet et al 2016</a> showed that low duty cycles can underestimate some species more than others.</p>
<p>What factors are most important to determine an appropriate duty cycle?</p>

<recording>
How to determine an appropriate duty cycle for a dataset?
# How to determine an appropriate duty cycle for a dataset?

(Asked by: username_0 on 2022-07-11)

Acoustic monitoring is limited by available power and memory storage, but duty cycling recordings can extend the time period that is monitored. I understand that choosing an apporpriate duty cycle will depend on your species and research question, but want to make sure I'm making the best decision. For example,

[Rand et al 2020](https://asa.scitation.org/doi/full/10.1121/10.0009752?casa_token=fPpzNpwnJmgAAAAA%3A3sRfakdhPowKj76e9wxTqiP9WceCn8teV8b0_rJFT7TjAjJ8cTmnC7F7Pq_lz52pnV03TbMqXJte) found that their ability to detect killer whale ecotypes and event durations was impacted by different duty cycles, but there was little impact on their ability to detect daily presence.

For beaked whales, [Stanistreet et al 2016](https://asa.scitation.org/doi/full/10.1121/1.4955009) showed that low duty cycles can underestimate some species more than others.

What factors are most important to determine an appropriate duty cycle?

**Tags:** recording


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## 3 answers

### Answer by username_3 on 2022-07-12. Score: 12 | Accepted answer

This is a really interesting question and there probably isn't a 'perfect' duty cycle.

The main two things to consider are

1. How much continuous data do you need to make a robust decision about the presence of a particular species.
2. How similar (auto-correlated) are adjacent chunks of time likely to be.

So for a species which has long duration calls which may not occur very often, you may want quite a long recording period. For example, blue whale calls can be up to 18s long, and intervals between calls my be over a minute, so you'd want individual recordings to be many minutes long if you require several calls to make a robust decision. However, blue whales are detectable at long ranges, so if you can hear a blue whale in one 10 minute time slot, it's also likely you'll hear it in the next which doesn't really add much data (auto-correlation), so why not record 10 minutes every half hour ?

For a species which produces many calls per second (e.g. porpoises, bats), but may only be in range for a few seconds as they pass the recorder, a much shorter duty cycle would probably be more appropriate so as to reduce the chances of missing passing animals completely.

This last point does of course raise the question of the statistical analysis you intend to use with your data once it's collected. With a long duty cycle, you might argue that you have periods when you detect everything and periods when you detect nothing, so the duty cycle becomes little more than a simple scaling factor. With very short duty cycles, this may not be so clear cut since you'll have to more carefully consider animal movement speed and detection range and think about what the chances are of missing animals completely. This may be an argument for longer cycles, accepting that you will miss animals, but the statistical analysis may be simpler.

Final things to consider may be the sheer number of files you'll generate with short duty cycles, the time it takes the recording device to start up, and possibly also how long any automatic detectors you're using may take to 'settle' (i.e. get a reasonable measure of background noise to set detection thresholds against) before they work as advertised.

> **Comments:**
> **username_4**: username_3 makes many excellent points.  To address many of these, to optimise sampling and to understand its statistical consequences, it will be very useful to have some continuous, completely analysed data sets which you could then sample using different protocols.  

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### Answer by username_1 on 2022-07-12. Score: 9

Choosing duty cycles is a sort of sampling.
Therefore you should choose your duty cycle such that you do not undersample the feature you would like to study. I would suggest to have at least one or two samples over the period of feature.

Example: if the beaked whales echolocate for 30 minutes every 2 hours, then you should have one sampling period every 20 minutes (< 30 min), better every 15 min.


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### Answer by username_2 on 2022-07-12. Score: 1

One important factor to consider is simply the likely presence of the signal of interest across a 24-hour period. For example, if you are targeting a nocturnal species, you may wish to start your recordings 30 minutes before sunset and end 30 minutes after sunrise, avoiding significant periods of likely-empty data.


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