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22 results for “acoustic indices”
Data for "Sounding out Ecoacoustic Metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats"
<p>This deposit contains the data for the paper <strong>A Multi-habitat, Comparative Evaluation of Ecoacoustic Indices for Biodiversity Monitoring: Acoustic Indices Predict Avian Species Richness in Temperate but not Tropical Habitats. (Ecological Indicators) </strong>The dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each one has 26 acoustic indices calculated on it, and a full list of avian species and abundances and GPS data for each sample site.</p> <p>Abstract</p> <p>Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the development of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices across a wide range of ecological conditions. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithologists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (root-mean-square, spectral centroid and zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses suggest that AIs also track observed differences in habitat-dependent community composition and that each habitat has a distinct soundscape. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors contribute to predicting avian diversity in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking of habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks.</p> <p> </p> <p><strong>Sampling Methods (extract from paper)</strong></p> <p>Acoustic surveys were carried out along a gradient of habitat degradation (1 forested, 2 regenerating forest and 3 agricultural land) in South East (SE) England and North Western (NW) Ecuador. The six sites (UK1, UK2, UK3, EC1, EC2, EC3) were sampled consecutively from May 6th - Aug 25th 2015.</p> <p>All UK sites were in the county of Sussex, in SE England, an area of weald clays (Fig. 2, left) and included ancient woodland (UK1), regenerating farmland with patches of woodland (UK2) and a downland barley farm (UK3).1 min mono audio recordings made every 15 minutes at three different habitats in the UK</p> <p>Ten day acoustic surveys were carried out consecutively at each study site using 15 Wildlife Acoustics Song Meter audio field recorders. Sampling points were arranged in a grid at a minimum distance of 200 m to minimise pseudo replication (the sound of most species being attenuated over this distance in all biomes). Altitudinal range of sample points across sites was minimised in order to prevent introduction of extraneous, confounding gradients (UK varied between 10 m – 50 m and Ecuador 130 m – 390 m). Recording schedules captured 1 min every 15 min around the clock for 10 days at each site, resulting in 960 recordings at each of 15 sample points for 3 habitat types in 2 different climates (86,400 1 minute recordings in total). Data across the 15 sample points was pooled; inter-site variation was not explored in the current analyses. In the UK 3½ hours of each dawn chorus was sampled starting at 1 hour before sunrise. This range was determined to capture the onset, progression and peak of the dawn chorus, creating a temporal gradient. The equatorial dawn chorus is more compact and was sampled for 2¼ hours starting 15 mins before sunrise, capturing a comparable chorus onset and peak.</p> <p> </p> <p> </p>
Text-fig. 2. Metacheiromys marshii, AMNH 131777, drawing of basicranium in ventral view with isosurface from CT scans of left petrosal inserted (compare with Simpson 1931: fig. 7). Much of the mastoid exposure on the specimen's left side is damaged. Numbers 1 to 4 indicate depressions that based on the right side include a thin layer of entotympanic; 1 to 3 are between petrosal and basioccipital and 4 is petrosal only. The white arrow in the lower left passes through a canal between the petrosal and exoccipital for the auricular branch of the vagus nerve. Abbreviations: abX – grooves and foramina for auricular branch of vagus nerve, as – alisphenoid, astp – alisphenoid tympanic process, bo – basioccipital, bs – basisphenoid, eam – roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, es – epitympanic sinus of squamosal, fm – foramen magnum, fo – foramen ovale, gf – glenoid fossa, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, mtc – musculotubal canal, oc – occipital condyle, pa – porus acousticus (hidden), pas – parasphenoid, pgp – postglenoid process, pr – promontorium of petrosal, ps – presphenoid, smf – stylomastoid foramen, sof – superior orbital fissure, sq – squamosal, tca – tympanic canaliculus, th – tympanohyal, tm – tubular external acoustic meatus. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 2. Metacheiromys marshii, AMNH 131777, drawing of basicranium in ventral view with isosurface from CT scans of left petrosal inserted (compare with Simpson 1931: fig. 7). Much of the mastoid exposure on the specimen's left side is damaged. Numbers 1 to 4 indicate depressions that based on the right side include a thin layer of entotympanic; 1 to 3 are between petrosal and basioccipital and 4 is petrosal only. The white arrow in the lower left passes through a canal between the petrosal and exoccipital for the auricular branch of the vagus nerve. Abbreviations: abX – grooves and foramina for auricular branch of vagus nerve, as – alisphenoid, astp – alisphenoid tympanic process, bo – basioccipital, bs – basisphenoid, eam – roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, es – epitympanic sinus of squamosal, fm – foramen magnum, fo – foramen ovale, gf – glenoid fossa, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, mtc – musculotubal canal, oc – occipital condyle, pa – porus acousticus (hidden), pas – parasphenoid, pgp – postglenoid process, pr – promontorium of petrosal, ps – presphenoid, smf – stylomastoid foramen, sof – superior orbital fissure, sq – squamosal, tca – tympanic canaliculus, th – tympanohyal, tm – tubular external acoustic meatus.
Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets
<p>Acoustic indices combined with clustering and classification approaches have been increasingly used to automate identification of the presence of vocalizing taxa or acoustic events of interest. While most studies using this approach standardize data collection and study design parameters at the project or study level, recent trends in ecological research are to investigate patterns at regional or continental scales. Large-scale studies often require collaboration between research groups and integration of data from multiple sources to fulfill objectives, which can lead to variation in recording equipment and data collection protocols.</p> <p>Our objectives were to determine how analytical approaches and variation in data collection and processing that is typical of regional acoustic monitoring programs influences accuracy when identifying vocal activity in migratory breeding birds. We used data from three regional datasets in Northern Alberta, Northern British Columbia, and Southern and Central Yukon, Canada to investigate the effect of analytical framework, sample size, local species richness, and data collection variables on classification accuracy.</p> <p>We found supervised classification approaches to be the most effective, with boosted regression trees identifying vocal activity with a 92.0% accuracy and easily able to accommodate variation in data collection and processing parameters. We also provide recommendations on effectively processing large and heterogeneous datasets including sufficient sample size, accommodating nuisance variables, and selecting suitable model training data.</p> <p>The results presented in this study can help inform decisions in data collection, data processing, and study design and analysis, maximize performance and accuracy during analysis, and efficiently process large, heterogeneous datasets to answer questions at scales previously difficult to investigate.</p>
Ecoacoustic Study Design Variation: Impact on Acoustic Indices and AudioSet Fingerprints
<b>Description: </b><p>Acoustic Index and AudioSet Fingerprint data quantified derived from audio recorded between the 26th of February and the 2nd March 2019.<br><br>The original raw audio was compressed, shortened and temporally subset to replicate common inconsistencies in ecoacoustic studies. This data frame show how this experimental variation affects how soundscapes are quantified by Analytical Indices and the AudioSet Fingerprint</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="http://127.0.0.1:8000/projects/project_view/200"><b>3D Acoustics for Audio Monitoring of Rainforest Biodiversity </b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (NERC QMEE CDT Studentship, NE/P012345/1, <a href="http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A">http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="http://127.0.0.1:8000/datasets/xml_metadata?id=5153193">here</a></p><p><b>Files: </b>This consists of 1 file: Ecoacoustic_Method_Comparison.xlsx</p><p><b>Ecoacoustic_Method_Comparison.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Analytical index values under differing experimental conditions</b> (described in worksheet Analytical_Index_Data)</p><p>Description: This dataset contains all the analytical indices derived from audio under different experimenatal conditions</p><p>Number of fields: 16</p><p>Number of data rows: 87211</p><p>Fields: </p><ul><li><b>id.no</b>: Sample ID (Field type: id)</li><li><b>file.size</b>: File size as a % of uncompressed (Field type: numeric)</li><li><b>compression</b>: Compression level (Mp3) (Field type: ordered categorical)</li><li><b>frame.size</b>: Frame size (recording length) (Field type: ordered categorical)</li><li><b>site</b>: Field Site Location (Field type: location)</li><li><b>req.freq</b>: Recording Frequency (Field type: numeric)</li><li><b>date</b>: Date (Field type: date)</li><li><b>time</b>: Time ID (including subsamples) (Field type: id)</li><li><b>max.freq</b>: Nyquist (Maximum) frequency (Field type: numeric)</li><li><b>ACI</b>: Acoustic Complexity Index (Field type: numeric)</li><li><b>ADI</b>: Acoustic Diversity Index (Field type: numeric)</li><li><b>Aeev</b>: Acoustic Eveness (Field type: numeric)</li><li><b>Bio</b>: Biodiversity Index (Field type: numeric)</li><li><b>H</b>: Acoustic Entropy (Field type: numeric)</li><li><b>M</b>: Median of Acoustic Envelope (Field type: numeric)</li><li><b>NDSI</b>: Normalised Difference Soundscape Index (Field type: numeric)</li></ul></li><li><p><b>AudioSet Fingerprint values under differing experimental conditions</b> (described in worksheet AudioSet_Fingerprint_Data)</p><p>Description: This dataset contains all the audioset fingerprint values derived from audio under different experimenatal conditions</p><p>Number of fields: 137</p><p>Number of data rows: 87329</p><p>Fields: </p><ul><li><b>id.no</b>: Sample ID (Field type: id)</li><li><b>file.size</b>: File size as a % of uncompressed (Field type: numeric)</li><li><b>frame.size</b>: Frame size (recording length) (Field type: ordered categorical)</li><li><b>compression</b>: Compression level (Mp3) (Field type: ordered categorical)</li><li><b>site</b>: Field Site Location (Field type: location)</li><li><b>req.freq</b>: Recording Frequency (Field type: numeric)</li><li><b>date</b>: Date (Field type: date)</li><li><b>time</b>: Time ID (including subsamples) (Field type: id)</li><li><b>max.freq</b>: Nyquist (Maximum) frequency (Field type: numeric)</li><li><b>feat1</b>: Feature 1 (Field type: numeric)</li><li><b>feat2</b>: Feature 2 (Field type: numeric)</li><li><b>feat3</b>: Feature 3 (Field type: numeric)</li><li><b>feat4</b>: Feature 4 (Field type: numeric)</li><li><b>feat5</b>: Feature 5 (Field type: numeric)</li><li><b>feat6</b>: Feature 6 (Field type: numeric)</li><li><b>feat7</b>: Feature 7 (Field type: numeric)</li><li><b>feat8</b>: Feature 8 (Field type: numeric)</li><li><b>feat9</b>: Feature 9 (Field type: numeric)</li><li><b>feat10</b>: Feature 10 (Field type: numeric)</li><li><b>feat11</b>: Feature 11 (Field type: numeric)</li><li><b>feat12</b>: Feature 12 (Field type: numeric)</li><li><b>feat13</b>: Feature 13 (Field type: numeric)</li><li><b>feat14</b>: Feature 14 (Field type: numeric)</li><li><b>feat15</b>: Feature 15 (Field type: numeric)</li><li><b>feat16</b>: Feature 16 (Field type: numeric)</li><li><b>feat17</b>: Feature 17 (Field type: numeric)</li><li><b>feat18</b>: Feature 18 (Field type: numeric)</li><li><b>feat19</b>: Feature 19 (Field type: numeric)</li><li><b>feat20</b>: Feature 20 (Field type: numeric)</li><li><b>feat21</b>: Feature 21 (Field type: numeric)</li><li><b>feat22</b>: Feature 22 (Field type: numeric)</li><li><b>feat23</b>: Feature 23 (Field type: numeric)</li><li><b>feat24</b>: Feature 24 (Field type: numeric)</li><li><b>feat25</b>: Feature 25 (Field type: numeric)</li><li><b>feat26</b>: Feature 26 (Field type: numeric)</li><li><b>feat27</b>: Feature 27 (Field type: numeric)</li><li><b>feat28</b>: Feature 28 (Field type: numeric)</li><li><b>feat29</b>: Feature 29 (Field type: numeric)</li><li><b>feat30</b>: Feature 30 (Field type: numeric)</li><li><b>feat31</b>: Feature 31 (Field type: numeric)</li><li><b>feat32</b>: Feature 32 (Field type: numeric)</li><li><b>feat33</b>: Feature 33 (Field type: numeric)</li><li><b>feat34</b>: Feature 34 (Field type: numeric)</li><li><b>feat35</b>: Feature 35 (Field type: numeric)</li><li><b>feat36</b>: Feature 36 (Field type: numeric)</li><li><b>feat37</b>: Feature 37 (Field type: numeric)</li><li><b>feat38</b>: Feature 38 (Field type: numeric)</li><li><b>feat39</b>: Feature 39 (Field type: numeric)</li><li><b>feat40</b>: Feature 40 (Field type: numeric)</li><li><b>feat41</b>: Feature 41 (Field type: numeric)</li><li><b>feat42</b>: Feature 42 (Field type: numeric)</li><li><b>feat43</b>: Feature 43 (Field type: numeric)</li><li><b>feat44</b>: Feature 44 (Field type: numeric)</li><li><b>feat45</b>: Feature 45 (Field type: numeric)</li><li><b>feat46</b>: Feature 46 (Field type: numeric)</li><li><b>feat47</b>: Feature 47 (Field type: numeric)</li><li><b>feat48</b>: Feature 48 (Field type: numeric)</li><li><b>feat49</b>: Feature 49 (Field type: numeric)</li><li><b>feat50</b>: Feature 50 (Field type: numeric)</li><li><b>feat51</b>: Feature 51 (Field type: numeric)</li><li><b>feat52</b>: Feature 52 (Field type: numeric)</li><li><b>feat53</b>: Feature 53 (Field type: numeric)</li><li><b>feat54</b>: Feature 54 (Field type: numeric)</li><li><b>feat55</b>: Feature 55 (Field type: numeric)</li><li><b>feat56</b>: Feature 56 (Field type: numeric)</li><li><b>feat57</b>: Feature 57 (Field type: numeric)</li><li><b>feat58</b>: Feature 58 (Field type: numeric)</li><li><b>feat59</b>: Feature 59 (Field type: numeric)</li><li><b>feat60</b>: Feature 60 (Field type: numeric)</li><li><b>feat61</b>: Feature 61 (Field type: numeric)</li><li><b>feat62</b>: Feature 62 (Field type: numeric)</li><li><b>feat63</b>: Feature 63 (Field type: numeric)</li><li><b>feat64</b>: Feature 64 (Field type: numeric)</li><li><b>feat65</b>: Feature 65 (Field type: numeric)</li><li><b>feat66</b>: Feature 66 (Field type: numeric)</li><li><b>feat67</b>: Feature 67 (Field type: numeric)</li><li><b>feat68</b>: Feature 68 (Field type: numeric)</li><li><b>feat69</b>: Feature 69 (Field type: numeric)</li><li><b>feat70</b>: Feature 70 (Field type: numeric)</li><li><b>feat71</b>: Feature 71 (Field type: numeric)</li><li><b>feat72</b>: Feature 72 (Field type: numeric)</li><li><b>feat73</b>: Feature 73 (Field type: numeric)</li><li><b>feat74</b>: Feature 74 (Field type: numeric)</li><li><b>feat75</b>: Feature 75 (Field type: numeric)</li><li><b>feat76</b>: Feature 76 (Field type: numeric)</li><li><b>feat77</b>: Feature 77 (Field type: numeric)</li><li><b>feat78</b>: Feature 78 (Field type: numeric)</li><li><b>feat79</b>: Feature 79 (Field type: numeric)</li><li><b>feat80</b>: Feature 80 (Field type: numeric)</li><li><b>feat81</b>: Feature 81 (Field type: numeric)</li><li><b>feat82</b>: Feature 82 (Field type: numeric)</li><li><b>feat83</b>: Feature 83 (Field type: numeric)</li><li><b>feat84</b>: Feature 84 (Field type: numeric)</li><li><b>feat85</b>: Feature 85 (Field type: numeric)</li><li><b>feat86</b>: Feature 86 (Field type: numeric)</li><li><b>feat87</b>: Feature 87 (Field type: numeric)</li><li><b>feat88</b>: Feature 88 (Field type: numeric)</li><li><b>feat89</b>: Feature 89 (Field type: numeric)</li><li><b>feat90</b>: Feature 90 (Field type: numeric)</li><li><b>feat91</b>: Feature 91 (Field type: numeric)</li><li><b>feat92</b>: Feature 92 (Field type: numeric)</li><li><b>feat93</b>: Feature 93 (Field type: numeric)</li><li><b>feat94</b>: Feature 94 (Field type: numeric)</li><li><b>feat95</b>: Feature 95 (Field type: numeric)</li><li><b>feat96</b>: Feature 96 (Field type: numeric)</li><li><b>feat97</b>: Feature 97 (Field type: numeric)</li><li><b>feat98</b>: Feature 98 (Field type: numeric)</li><li><b>feat99</b>: Feature 99 (Field type: numeric)</li><li><b>feat100</b>: Feature 100 (Field type: numeric)</li><li><b>feat101</b>: Feature 101 (Field type: numeric)</li><li><b>feat102</b>: Feature 102 (Field type: numeric)</li><li><b>feat103</b>: Feature 103 (Field type: numeric)</li><li><b>feat104</b>: Feature 104 (Field type: numeric)</li><li><b>feat105</b>: Feature 105 (Field type: numeric)</li><li><b>feat106</b>: Feature 106 (Field type: numeric)</li><li><b>feat107</b>: Feature 107 (Field type: numeric)</li><li><b>feat108</b>: Feature 108 (Field type: numeric)</li><li><b>feat109</b>: Feature 109 (Field type: numeric)</li><li><b>feat110</b>: Feature 110 (Field type: numeric)</li><li><b>feat111</b>: Feature 111 (Field type: numeric)</li><li><b>feat112</b>: Feature 112 (Field type: numeric)</li><li><b>feat113</b>: Feature 113 (Field type: numeric)</li><li><b>feat114</b>: Feature 114 (Field type: numeric)</li><li><b>feat115</b>: Feature 115 (Field type: numeric)</li><li><b>feat116</b>: Feature 116 (Field type: numeric)</li><li><b>feat117</b>: Feature 117 (Field type: numeric)</li><li><b>feat118</b>: Feature 118 (Field type: numeric)</li><li><b>feat119</b>: Feature 119 (Field type: numeric)</li><li><b>feat120</b>: Feature 120 (Field type: numeric)</li><li><b>feat121</b>: Feature 121 (Field type: numeric)</li><li><b>feat122</b>: Feature 122 (Field type: numeric)</li><li><b>feat123</b>: Feature 123 (Field type: numeric)</li><li><b>feat124</b>: Feature 124 (Field type: numeric)</li><li><b>feat125</b>: Feature 125 (Field type: numeric)</li><li><b>feat126</b>: Feature 126 (Field type: numeric)</li><li><b>feat127</b>: Feature 127 (Field type: numeric)</li><li><b>feat128</b>: Feature 128 (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2019-02-26 to 2019-06-02</p><p><b>Latitudinal extent: </b>4.6644 to 4.7027</p><p><b>Longitudinal extent: </b>117.5351 to 117.5914</p>
Data from: Acoustic indices estimate breeding bird species richness with daily and seasonally variable effectiveness in lowland temperate Białowieża forest
<p><span>Biodiversity monitoring is important to follow temporal changes of the environment. We examined whether acoustic indices can be used as a rapid and easy-to-apply tool for bird biodiversity estimation in one of the least changed European lowland forests – the Białowieża Forest.</span></p> <p><span>We collected soundscape recordings in early and late spring at 84 randomly chosen recording points. At each recording point, we analysed 72 1-min sound samples to evaluate how well acoustic indices predict bird species richness from the perspective of a single sound sample, single survey, and recording point, and how they follow the daily pattern of singing activity. For each 1-min sound sample, we prepared a list of vocalizing bird species and calculated three acoustic indices: Bioacoustic Index (BI), Acoustic Complexity Index (ACI), and Acoustic Diversity Index (ADI)</span>.</p> <p><span>We found that from the perspective of a single 1-min sound sample, BI best predicts the bird species richness, independently of time in the season but variably across the day, while ACI and ADI showed weaker and seasonally and daily variable dependency. The correlation between each index and the number of bird species was stronger in the early survey than in the late survey. All acoustic indices followed daily bird activity patterns, yet they provided greater values before the peak of the species richness estimated by manual spectrogram scanning and listening to recordings.</span></p> <p><span>We showed that acoustic indices correlate moderately to strongly with the bird species richness obtained by manual spectrogram scanning and listening to recordings by humans. Therefore, acoustic indices can be used as a tool for rapid estimation of bird biodiversity in temperate forests. However, daily and seasonal variation in effectiveness of acoustic indices should be taken into account in the analysis.</span></p>
Passive acoustic monitoring indicates Barred Owls are established in northern coastal California and management intervention is warranted
<p>Barred Owls (<em>Strix varia</em>) have recently expanded westward from eastern North America, contributing to substantial declines in Northern Spotted Owls (<em>Strix occidentalis caurina</em>). Passive acoustic monitoring (PAM) represents a potentially powerful approach for tracking range expansions like the Barred Owl's, but further methods development is needed to ensure that PAM-informed occupancy models meaningfully reflect population processes. Focusing on the leading edge of the Barred Owl range expansion in coastal California, we used a combination of PAM data, GPS-tagging, and active surveys to (1) estimate breeding home range size, (2) identify patterns of vocal activity that reflect resident occupancy, and (3) estimate resident occupancy rates. Mean breeding season home range size (452 ha) was reasonably consistent with the size of cells (400 ha) sampled with autonomous recording units (ARUs). Nevertheless, false-positive acoustic detections of Barred Owls frequently occurred within cells not containing an activity center such that site occupancy estimates derived using all detected vocalizations (0.61) were unlikely to be representative of resident occupancy. However, the proportion of survey nights with confirmed vocalizations (VN) and the number of ARUs within a sampling cell with confirmed vocalizations (VU) were indicative of Barred Owl residency. Moreover, the false positive error rate could be reduced for occupancy analyses by establishing thresholds of VN and VU to define detections, although doing so increased false negative error rates in some cases. Using different thresholds of VN and VU, we estimated resident occupancy to be 0.29–0.44, which indicates that Barred Owls have become established in the region but also that timely lethal removals could still help prevent the extirpation of Northern Spotted Owls. Our findings provide a scalable framework for monitoring Barred Owl populations throughout their expanded range and, more broadly, a basis for converting site occupancy to resident occupancy in PAM programs. </p>
Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets
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Passive acoustic monitoring indicates Barred Owls are established in northern coastal California and management intervention is warranted
Open the record for dataset details and reuse information.
The impact of vehicular noise on the effectiveness of acoustic indices
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Data from: Acoustic indices estimate breeding bird species richness with daily and seasonally variable effectiveness in lowland temperate Białowieża forest
Open the record for dataset details and reuse information.
Utility of acoustic indices for ecological monitoring in complex sonic environments
<p>Abstract</p> <p>With the continued adoption of passive acoustic monitoring as a tool for rapid and high-resolution ecosystem monitoring, ecologists are increasingly making use of a suite of acoustic indices to summarise the sonic environment. Though these indices are often reported to well represent some aspect of the biology of an ecosystem, the degree to which they are confounded by various extraneous sonic conditions is largely unknown. We conducted an aural inventory across 23 field sites in Okinawa to identify the number of unique animal sounds present in recordings. Using these values of ‘measured richness’, we then examined how the performance of 11 commonly-used acoustic indices varied across a range of sonic conditions (including in the presence and absence of insect stridulations, audible wind or rain, and human-related sounds). Our analysis identified both well- and poor-performing acoustic indices, as well as those that were particularly sensitive to sonic conditions. Only two indices reflected measured richness across the full range of sonic conditions examined. A few indices were relatively insensitive to extraneous sonic conditions, but no index correlated with measured richness when masked by sound from broadband stridulating insects. Our results demonstrate considerable sensitivity of most commonly used acoustic indices to confounding sonic conditions, highlighting the challenges of working with large acoustic datasets collected in the field. We make practical recommendations for acoustic index use based on study design, with the aim of identifying the suite of acoustic indices with greatest utility as indicators for rapid biodiversity monitoring and management of the world’s natural soundscapes.</p> <p>Methods</p> <p>The dataset contains the names of audio files collected across 23 field sites between April 2017 and January 2018 as part of the OKEON-Churamori project on the island of Okinawa, Japan. We conducted an aural inventory, manually counting and recording the number of unique biotic sounds (approximately corresponding to species richness) and noting the presence or absence of three potentially confounding sonic conditions: audible geophony (wind, rain etc.), anthropophony (human-related sounds), and broadband sounds produced by stridulating insect (e.g. cicadas, orthopterans). We then calculated 11 commonly used acoustic indices from the literature and compared their performance (correlation with richness) in the presence vs absence of each sonic condition. Our dataset also contains time and date information for each recording, and the mean site-level richness (i.e. across multiple recordings) for each site and for each unique site-by-season combination. See Table A2 and Methods section in the associated manuscript for details on data processing and the calculation of acoustic indices.</p> <p>Usage notes</p> <p>See readme file for descriptions of data table structure.</p>
Acoustic indices perform better when applied at ecologically meaningful time and frequency scales
Abstract: 1. Acoustic indices are increasingly employed in the analysis of soundscapes to ascertain biodiversity value. However, conflicting results and lack of consensus on best practices for their usage has hindered their application in conservation and land-use management contexts. Here we propose that the sensitivity of acoustic indices to ecological change and fidelity of acoustic indices to ecological communities are severely impacted by signal masking. Signal masking can occur when acoustic responses sensitive to the effect being monitored are masked by less sensitive acoustic groups, or target taxa sonification is masked by non-target noise. We argue that by calculating acoustic indices at ecologically appropriate time and frequency bins, masking effects can be reduced and the efficacy of indices increased. 2. We test this on a large acoustic dataset collected in Eastern Amazonia spanning a disturbance gradient including undisturbed, logged, burned, logged-and-burned, and secondary forests. We calculated values for two acoustic indices: the Acoustic Complexity Index and the Bioacoustic Index, across the entire frequency spectrum (0-22.1 kHz), and four narrower subsets of the frequency spectrum; at dawn, day, dusk and night. 3. We show that signal masking has a large impact on the sensitivity of acoustic indices to forest disturbance classes. Calculating acoustic indices at a range of narrower time-frequency bins substantially increases the classification accuracy of forest classes by random forest models. Furthermore, signal masking led to highly misleading correlations, including spurious inverse correlations, between biodiversity indicator metrics and acoustic index values compared to correlations derived from manual sampling of the audio data. 4. Consequently, we recommend that acoustic indices are calculated either at a range of time and frequency bins, or at a single narrow bin, predetermined by a priori ecological understanding of the soundscape.
FIGURE4. Maximum-likelihood tree inferred from 694 bp of COI using a HKY+G substitution model implemented in MEGAX (Kumar et al. 2018). Bootstrap values are indicated on the nodes. in --Molecular--and--acoustic--evidence--support--the--species--status--of--Anthus rubescens rubescens and--Anthus [rubescens] japonicus--(Passeriformes:--Motacillidae)
FIGURE4. Maximum-likelihood tree inferred from 694 bp of COI using a HKY+G substitution model implemented in MEGAX (Kumar et al. 2018). Bootstrap values are indicated on the nodes.
FIGURE3. Maximum-likelihood tree inferred from 998 bp of CR using a HKY+G substitution model implemented in MEGAX (Kumar et al. 2018). Bootstrap values are indicated on the nodes. in --Molecular--and--acoustic--evidence--support--the--species--status--of--Anthus rubescens rubescens and--Anthus [rubescens] japonicus--(Passeriformes:--Motacillidae)
FIGURE3. Maximum-likelihood tree inferred from 998 bp of CR using a HKY+G substitution model implemented in MEGAX (Kumar et al. 2018). Bootstrap values are indicated on the nodes.
FIGURE1. Breeding, migrating and wintering distributions of Palearctic Anthus [rubescens] japonicus and Nearctic Anthus rubescens rubescens/alticola subspecies groups (from BirdLife International 2022; illustration @Andrew Birch). Circles indicate origins of sequenced individuals and triangles indicate origins of analysed recordings of calls. Localities outside of the usual range of the species complex (e.g., Ireland, Oman and Israel) are not figured here. in --Molecular--and--acoustic--evidence--support--the--species--status--of--Anthus rubescens rubescens and--Anthus [rubescens] japonicus--(Passeriformes:--Motacillidae)
FIGURE1. Breeding, migrating and wintering distributions of Palearctic Anthus [rubescens] japonicus and Nearctic Anthus rubescens rubescens/alticola subspecies groups (from BirdLife International 2022; illustration @Andrew Birch). Circles indicate origins of sequenced individuals and triangles indicate origins of analysed recordings of calls. Localities outside of the usual range of the species complex (e.g., Ireland, Oman and Israel) are not figured here.
Acoustic indices perform better when applied at ecologically meaningful time and frequency scales
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Data from: Phenotypic variation and covariation indicate high evolvability of acoustic communication in crickets
Studying the genetic architecture of sexual traits provides insight into the rate and direction at which traits can respond to selection. Traits associated with few loci and limited genetic and phenotypic constraints tend to evolve at high rates typically observed for secondary sexual characters. Here, we examined the genetic architecture of song traits and female song preferences in the field crickets Gryllus rubens and G. texensis. Song and preference data were collected from both species and interspecific F1 and F2 hybrids. We first analysed phenotypic variation to examine interspecific differentiation and trait distributions in parental and hybrid generations. Then, the relative contribution of additive and additive-dominance variation was estimated. Finally, phenotypic variance-covariance (P) matrices were estimated to evaluate the multivariate phenotype available for selection. Song traits and preferences had unimodal trait distributions and hybrid offspring were intermediate with respect to the parents. We uncovered additive and dominance variation in song traits and preferences. For two song traits we found evidence for X-linked inheritance. On one hand, the observed genetic architecture does not suggest rapid divergence, although sex-linkage may have allowed for somewhat higher evolutionary rates. On the other hand, P matrices revealed that multivariate variation in song traits aligned with major dimensions in song preferences, suggesting a strong selection response. We also found strong covariance between the main traits that are sexually selected and traits that are not directly selected by females, providing an explanation for the striking multivariate divergence in male calling songs despite limited divergence in female preferences.
Assessment of acoustic indices for monitoring phylogenetic and temporal patterns of biodiversity in tropical forests
<b>Description: </b><p>Acostic recordings of bird calls at SAFE in 2014. Four solar-powered Wildlife Song Meter SM3 bioacoustic recorders (Wildlife Acoustics Inc., Concord,<br>MA, USA) were deployed with omni-directional microphones (sensitivity: 20Hz - 20kHz). No exact locations recorded.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/175"><b>Continuous bio-acoustic monitoring (2020 extension)</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7740620">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Trigg_safedata.xlsx, CLIPS.zip</p><p><b>Trigg_safedata.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Data</b> (described in worksheet Data)</p><p>Description: excel sheet with clips listed and birds identified</p><p>Number of fields: 7</p><p>Number of data rows: 837</p><p>Fields: </p><ul><li><b>FileNumber</b>: The audio files, which are labelled 1 to 120 (Field type: id)</li><li><b>StartTime</b>: approximate time when the species call began. (Field type: numeric)</li><li><b>EndTime</b>: approximate end time of the call. (Field type: numeric)</li><li><b>original_taxon_names</b>: species that can be heard in the recording. Original version as entered in to worksheet (Field type: comments)</li><li><b>Species</b>: species that can be heard in the recording. Corrected name, spelling mistakes and excess spaces removed etc. (Field type: taxa)</li><li><b>CallType</b>: Type of sound being identified (Field type: categorical)</li><li><b>IdCertainty</b>: Degree of certainty (Field type: categorical)</li></ul></li></ol><p><b>CLIPS.zip</b></p><p>Description: Zip file containing original audio clips used to generate these data</p><p><b>Date range: </b>2015-04-01 to 2015-06-30</p><p><b>Latitudinal extent: </b>4.6000 to 4.8000</p><p><b>Longitudinal extent: </b>117.5000 to 117.7000</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Primates <br> -  -  -  -  -  Hylobatidae <br> -  -  -  -  -  -  <i>Hylobates</i> <br> -  -  -  -  -  -  -  <i>Hylobates funereus</i> <br> -  -  -  Aves <br> -  -  -  -  Galliformes <br> -  -  -  -  -  Phasianidae <br> -  -  -  -  -  -  <i>Argusianus</i> <br> -  -  -  -  -  -  -  <i>Argusianus argus</i> <br> -  -  -  -  Bucerotiformes <br> -  -  -  -  -  Bucerotidae <br> -  -  -  -  -  -  <i>Anorrhinus</i> <br> -  -  -  -  -  -  -  <i>Anorrhinus galeritus</i> <br> -  -  -  -  -  -  <i>Buceros</i> <br> -  -  -  -  -  -  -  <i>Buceros rhinoceros</i> <br> -  -  -  -  -  -  <i>Rhinoplax</i> <br> -  -  -  -  -  -  -  <i>Rhinoplax vigil</i> <br> -  -  -  -  Psittaciformes <br> -  -  -  -  -  Psittacidae <br> -  -  -  -  -  -  <i>Loriculus</i> <br> -  -  -  -  -  -  -  <i>Loriculus galgulus</i> <br> -  -  -  -  Passeriformes <br> -  -  -  -  -  Cisticolidae <br> -  -  -  -  -  -  <i>Orthotomus</i> <br> -  -  -  -  -  -  -  <i>Orthotomus ruficeps</i> <br> -  -  -  -  -  -  -  <i>Orthotomus atrogularis</i> <br> -  -  -  -  -  -  -  <i>Orthotomus sericeus</i> <br> -  -  -  -  -  -  <i>Prinia</i> <br> -  -  -  -  -  -  -  <i>Prinia flaviventris</i> <br> -  -  -  -  -  Chloropseidae <br> -  -  -  -  -  -  <i>Chloropsis</i> <br> -  -  -  -  -  -  -  <i>Chloropsis cyanopogon</i> <br> -  -  -  -  -  Aegithinidae <br> -  -  -  -  -  -  <i>Aegithina</i> <br> -  -  -  -  -  -  -  <i>Aegithina viridissima</i> <br> -  -  -  -  -  Pycnonotidae <br> -  -  -  -  -  -  <i>Pycnonotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus atriceps</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus simplex</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus erythropthalmos</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus goiavier</i> <br> -  -  -  -  -  -  <i>Alophoixus</i> <br> -  -  -  -  -  -  -  <i>Alophoixus bres</i> <br> -  -  -  -  -  -  <i>Iole</i> <br> -  -  -  -  -  -  -  <i>Iole olivacea</i> <br> -  -  -  -  -  Dicaeidae <br> -  -  -  -  -  -  <i>Prionochilus</i> <br> -  -  -  -  -  -  -  <i>Prionochilus xanthopygius</i> <br> -  -  -  -  -  -  <i>Dicaeum</i> <br> -  -  -  -  -  -  -  <i>Dicaeum trigonostigma</i> <br> -  -  -  -  -  Monarchidae <br> -  -  -  -  -  -  <i>Hypothymis</i> <br> -  -  -  -  -  -  -  <i>Hypothymis azurea</i> <br> -  -  -  -  -  -  <i>Rhipidura</i> <br> -  -  -  -  -  -  -  <i>Rhipidura javanica</i> <br> -  -  -  -  -  Irenidae <br> -  -  -  -  -  -  <i>Irena</i> <br> -  -  -  -  -  -  -  <i>Irena puella</i> <br> -  -  -  -  -  Muscicapidae <br> -  -  -  -  -  -  <i>Trichixos</i> <br> -  -  -  -  -  -  -  <i>Trichixos pyrropygus</i> <br> -  -  -  -  -  Tephrodornithidae <br> -  -  -  -  -  -  <i>Hemipus</i> <br> -  -  -  -  -  -  -  <i>Hemipus picatus</i> <br> -  -  -  -  -  -  -  <i>Hemipus hirundinaceus</i> <br> -  -  -  -  -  Pellorneidae <br> -  -  -  -  -  -  <i>Kenopia</i> <br> -  -  -  -  -  -  -  <i>Kenopia striata</i> <br> -  -  -  -  -  -  <i>Pellorneum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum capistratum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum bicolor</i> <br> -  -  -  -  -  -  <i>Malacopteron</i> <br> -  -  -  -  -  -  -  <i>Malacopteron affine</i> <br> -  -  -  -  -  -  <i>Malacocincla</i> <br> -  -  -  -  -  -  -  <i>Malacocincla malaccensis</i> <br> -  -  -  -  -  -  <i>Alcippe</i> <br> -  -  -  -  -  -  -  <i>Alcippe brunneicauda</i> <br> -  -  -  -  -  Corvidae <br> -  -  -  -  -  -  <i>Corvus</i> <br> -  -  -  -  -  -  -  <i>Corvus enca</i> <br> -  -  -  -  -  Acanthizidae <br> -  -  -  -  -  -  <i>Gerygone</i> <br> -  -  -  -  -  -  -  <i>Gerygone sulphurea</i> <br> -  -  -  -  -  Timaliidae <br> -  -  -  -  -  -  <i>Cyanoderma</i> <br> -  -  -  -  -  -  -  <i>Cyanoderma erythropterum</i> <br> -  -  -  -  -  -  -  <i>Cyanoderma rufifrons</i> <br> -  -  -  -  -  -  <i>Mixornis</i> <br> -  -  -  -  -  -  -  <i>Mixornis bornensis</i> <br> -  -  -  -  -  -  <i>Macronus</i> <br> -  -  -  -  -  -  -  <i>Macronus ptilosus</i> <br> -  -  -  -  -  -  <i>Stachyris</i> <br> -  -  -  -  -  -  -  <i>Stachyris maculata</i> <br> -  -  -  -  -  -  -  <i>Stachyris poliocephala</i> <br> -  -  -  -  -  -  <i>Pomatorhinus</i> <br> -  -  -  -  -  -  -  <i>Pomatorhinus montanus</i> <br> -  -  -  -  -  Nectariniidae <br> -  -  -  -  -  -  <i>Anthreptes</i> <br> -  -  -  -  -  -  -  <i>Anthreptes simplex</i> <br> -  -  -  -  -  -  <i>Arachnothera</i> <br> -  -  -  -  -  -  -  <i>Arachnothera longirostra</i> <br> -  -  -  -  -  -  -  <i>Arachnothera affinis</i> <br> -  -  -  -  -  Eurylaimidae <br> -  -  -  -  -  -  <i>Eurylaimus</i> <br> -  -  -  -  -  -  -  <i>Eurylaimus ochromalus</i> <br> -  -  -  -  Piciformes <br> -  -  -  -  -  Ramphastidae <br> -  -  -  -  -  -  <i>Megalaima</i> <br> -  -  -  -  -  -  -  <i>Megalaima australis</i> <br> -  -  -  -  -  -  -  -  <i>Megalaima australis duvaucelii</i> (as homotypic_synonym: <i>Psilopogon duvaucelii</i>)<br> -  -  -  -  -  Picidae <br> -  -  -  -  -  -  <i>Meiglyptes</i> <br> -  -  -  -  -  -  -  <i>Meiglyptes tukki</i> <br> -  -  -  -  -  -  <i>Dinopium</i> <br> -  -  -  -  -  -  -  <i>Dinopium rafflesii</i> <br> -  -  -  -  -  -  <i>Blythipicus</i> <br> -  -  -  -  -  -  -  <i>Blythipicus rubiginosus</i> <br> -  -  -  -  -  -  <i>Mulleripicus</i> <br> -  -  -  -  -  -  -  <i>Mulleripicus pulverulentus</i> <br> -  -  -  -  Cuculiformes <br> -  -  -  -  -  Cuculidae <br> -  -  -  -  -  -  <i>Cacomantis</i> <br> -  -  -  -  -  -  -  <i>Cacomantis sonneratii</i> <br> -  -  -  -  -  -  -  <i>Cacomantis merulinus</i> <br> -  -  -  -  -  -  <i>Rhinortha</i> <br> -  -  -  -  -  -  -  <i>Rhinortha chlorophaea</i> <br> -  -  -  -  -  -  <i>Cuculus</i> <br> -  -  -  -  -  -  -  <i>Cuculus vagans</i> <br> -  -  -  -  -  -  <i>Centropus</i> <br> -  -  -  -  -  -  -  <i>Centropus sinensis</i> <br> -  -  -  -  Columbiformes <br> -  -  -  -  -  Columbidae <br> -  -  -  -  -  -  <i>Chalcophaps</i> <br> -  -  -  -  -  -  -  <i>Chalcophaps indica</i> <br></div><p></p>
Data from: Phenotypic variation and covariation indicate high evolvability of acoustic communication in crickets
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Raw data acoustic indices used for four indigenous communities in the Sierra Nevada de Santa Marta
<p>Raw data acoustic indices used for four indigenous communities in the Sierra Nevada de Santa Marta</p>
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