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5 results for “Ecoacoustic 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>
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>
Ecoacoustic Indices and Psychoacoustic Perception Dataset
<p>Companion Dataset for the Publication: Linking ecoacoustic indices to psychoacoustic perception of the urban acoustic environment</p>
Data from: Ecoacoustic indices as proxies for biodiversity on temperate reefs
Diversity measurement techniques can present logistical and financial obstacles to conservation efforts. Ecoacoustics has recently emerged as a promising solution to these issues, providing a mechanism for measuring diversity using acoustic indices, which have proven to be beneficial in terrestrial habitats. This study investigates the application of acoustic measures as a tool for quick and effective marine diversity monitoring via direct, in situ comparison of ecoacoustics indices with species assemblage diversity measures from temperate rocky reefs. Acoustic recordings and visual surveys of reef fish abundance were collected at nine sites in north-eastern New Zealand. Three ecoacoustic indices originally developed for terrestrial use were then compared to three species assemblage diversity measures and compared using Pearson correlations. Additionally, four criteria for successful ecoacoustic indices were developed and tested as a means of standardizing future evaluation and use of acoustic indices: (i) positive correlations between species diversity and ecoacoustic indices in relevant frequency ranges, (ii) robustness to changes in spectral resolution, (iii) robustness to the presence of natural noise interference (i.e. wind) and (iv) robustness to the presence of anthropogenic noise. Acoustic Complexity Index (ACI) was significantly correlated with Pielou's Evenness (J′) and Shannon's index (H′). Neither Acoustic Richness (AR) nor ACI was impacted by changes in spectral resolution, but values of the Acoustic Entropy Index (H) increased significantly between fast Fourier transformation (FFT) sizes 512 and 1024. H was consistently positively correlated with both H′ and estimated number of species (S) above a spectral resolution of c. 140·6 Hz (FFT size 1024). Wind did not affect any of the acoustic indices. As anthropogenic noise was included in these investigations, both ACI and H were considered robust to its presence. While AR failed to meet all four criteria for a successful ecoacoustic indices, both ACI and H appeared to be appropriate for use on temperate reefs. In a time of accelerated global diversity loss, these two ecoacoustic indices show strong potential for use as efficient, non-invasive marine diversity measures.
Data from: Ecoacoustic indices as proxies for biodiversity on temperate reefs
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