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4 results for “nonlinear vocalizations”
The impact of parasitic infection on acoustic communication and mate choice in two torrent frogs with nonlinear vocalization
<p><span>For frogs with linear vocalization, Hamilton and Zuk's hypothesis indicated that parasites adversely affect male sex characters and </span><span>parasite infection can result in sexual signals that are less intense or conspicuous than in healthy individuals, which causes females to prefer unparasitized over parasitized males. </span><span>In order to investigate the effects of parasite infection on vocal characteristics and mate choice in frogs, which can emit nonlinear phenomena calls (NLP). The calls of the two species of torrent male frog, namely the concave-eared torrent frog </span><span>(<em>Odorrana</em> <em>tormota</em>)</span><span> and the large odorous frog </span><span>(<em>Odorrana</em> <em>graminea</em>), </span><span>were recorded, and several vocal parameters (calling duration, calling rate, average fundamental frequency, NLP duration and NLP content) and parasite intensity were analyzed in detail. The results revealed a negative relationship between calling rate and parasite intensity and a positive relationship between NLP content and parasite intensity. Furthermore, the effects of parasite intensity on amplectant showed that parasite intensity from amplexed pairs was higher than that of solo males in the vicinity of the amplexus. This might be because both male frogs with higher parasite intensity can emit calls with higher NLP content, and it has been shown that females have a preference for high NLP content. </span><span>This study confirms that parasites can influence the NLP content in both torrent frog' calls, which in turn affects the host' mate choice. The current study suggests effects of parasite infection on mate choice in host frogs might be influenced by vocal manner, which provides new insight into the parasite-host relationship.</span></p>
The impact of parasitic infection on acoustic communication and mate choice in two torrent frogs with nonlinear vocalization
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Nonlinear vocal phenomena in the begging calls of the African penguin
<p>This repository hosts the dataset used in the analysis conducted for the article titled "Nonlinear Vocal Phenomena in the Begging Calls of the African Penguin," including all the necessary files to replicate the analysis and findings presented in the study.</p> <p> </p> <p><strong>Files information</strong></p> <ul> <li><strong>Dataset_full_model.csv = model 1</strong><br>The variable inside are: <br>file_ name= name of the file <br>seq= sequences' Identity<br>id = chicks' identity<br>status = healthy or sick<br>age = chicks age in day<br>nlp= presence/absence<br><br></li> <li><strong>Dataset_control_model.csv = model 2</strong><br>file_ name= name of the file <br>seq= sequences' Identity<br>id = chicks' identity<br>status = healthy or sick<br>age = chicks age in day<br>nlp= nlp presence/absence<br><br></li> <li><strong>Dataset_reduced_NLP_category.csv = model 3, model 4, model 5</strong><br>file_ name= name of the file <br>seq= sequences' Identity<br>id = chicks' identity<br>status = healthy or sick<br>age = chicks age in day<br>nlp_ch=nlp chaos presence/absence<br>nlp_sb=nlp sidebands presence/absence<br>multi = presence of subharmonics, frequency jump or both<br><br></li> <li><strong>Dataset_reduced_NLP_duration.csv = model 6</strong><br>file_ name= name of the file <br>seq= sequences' Identity<br>id = chicks' identity<br>status = healthy or sick<br>age = chicks age in day<br>freq= duration of the NLP normalised on the call duration</li> </ul> <p> </p>
Nonlinear vocal phenomena in African penguin begging calls: occurrence, significance, and potential applications
<p>This repository hosts the dataset used in the analysis conducted for the article titled "Nonlinear vocal phenomena in African penguin begging calls: occurrence, significance, and potential applications" including all the necessary files to replicate the analysis and findings presented in the study.</p> <p> </p> <p><strong>Files information</strong></p> <ul> <li><strong>Dataset_full_model.csv = model 1</strong><br>The variable inside are: <br>file_ name= name of the file <br>seq= sequences' Identity<br>id = chicks' identity<br>status = healthy or sick<br>age = chicks age in day<br>nlp= presence/absence<br><br></li> <li><strong>Dataset_control_model.csv = model 2</strong><br>file_ name= name of the file <br>seq= sequences' Identity<br>id = chicks' identity<br>status = healthy or sick<br>age = chicks age in day<br>nlp= nlp presence/absence<br><br></li> <li><strong>Dataset_reduced_NLP_category.csv = model 3, model 4, model 5</strong><br>file_ name= name of the file <br>seq= sequences' Identity<br>id = chicks' identity<br>status = healthy or sick<br>age = chicks age in day<br>nlp_ch=nlp chaos presence/absence<br>nlp_sb=nlp sidebands presence/absence<br>multi = presence of subharmonics, frequency jump or both<br><br></li> <li><strong>Dataset_reduced_NLP_duration.csv = model 6</strong><br>file_ name= name of the file <br>seq= sequences' Identity<br>id = chicks' identity<br>status = healthy or sick<br>age = chicks age in day<br>freq= duration of the NLP normalised on the call duration</li> </ul> <p> </p>
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