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opencc-by-4.0Dec 2015View details →

FIGURE 6 in INTRASPECIFIC VARIATION IN ACOUSTIC TRAITS AND BODY SIZE, AND NEW DISTRIBUTIONAL RECORDS FOR PSEUDOPALUDICOLA GIARETTAI CARVALHO, 2012 (ANURA, LEPTODACTYLIDAE, LEIUPERINAE): IMPLICATIONS FOR ITS CONGENERIC DIAGNOSIS

FIGURE 6: Typical breeding habitat of P. giarettai: a permanent pond associated with a Buriti palm grove marsh at the Parque Nacional Grande Sertão Veredas (Municipality of Chapada Gaúcha), northwestern Minas Gerais, southeastern Brazil.

opencc-by-4.0Dec 2015View details →

FIGURE 1 in INTRASPECIFIC VARIATION IN ACOUSTIC TRAITS AND BODY SIZE, AND NEW DISTRIBUTIONAL RECORDS FOR PSEUDOPALUDICOLA GIARETTAI CARVALHO, 2012 (ANURA, LEPTODACTYLIDAE, LEIUPERINAE): IMPLICATIONS FOR ITS CONGENERIC DIAGNOSIS

FIGURE 1: Distribution map of Pseudopaludicola giarettai. Star (Type locality: Curvelo, MG); Circles (localities in Minas Gerais): 1 (Buritis), 2 (Chapada Gaúcha), 3 (Unaí), 4 (Buritizeiro), 5 (Coromandel), 6 (Conceição do Mato Dentro; Pimenta et al., 2014), 7 (Muriaé; Santana et al., 2009); Triangle (Flores de Goiás, GO). MG = Minas Gerais; GO = Goiás.

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Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings

<p>The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in forests of Pennsylvania during the Spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle-Nichols and time-to-detection models to estimate abundance of two species from detection histories generated by applying a machine-learning classifier to ARU-gathered data. We compared abundance estimates from these models to estimates from the same models fit using point-count data and to two additional models appropriate for point counts, the N-mixture model and distance models. We found that the Royle-Nichols and time-to-detection models can be used with ARU data to produce abundance estimates similar to those generated by a point-count based study but with greater precision. ARU-based models produced confidence or credible intervals that were on average 31.9% ( 11.9 SE) smaller than their point-count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine-learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support the use of ARUs in abundance-based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation actions and recommendations.</p>

opencc-zeroJul 2024View details →

Fig. 6 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 6 Comparison of the duty cycle in the songs of the T. armeniaca complex and T. caudata (left panel) and the Tettigonia viridissima group (right panel)

Fig. 2 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 2 Oscillograms of the song of the Tettigonia viridissima group (1–9) and T. cantans (10) recorded at two speeds: 1 T. cf. longealata (MO: Ajabo, T = 20 °C), 2 T. cf. vaucheriana (MO: N Fes, T = 20 °C), 3 T. cf. vaucheriana (MO: Bouchfaa W of Taza, T = 21 °C), 4 T. cf. vaucheriana (MO: Tilougguite Pass, T = 23 °C), 5 T. cf. vaucheriana and cf. longealata (MO: El Kebab, T = 25 °C), 6 T. cf. vaucheriana (MO: El Kebab, T = 28–30 °C), 7 T. cf. viridissima (MO: S Aïn Zora, T = 22 °C), 8 T. cf. viridissima (MO: S Aïn Zora, T = 25 °C), 9 T. viridissima (BG: Sofia, T = 27 °C), and 10 T. cantans (IT: Val Malene; from Massa et al. 2012, T = 15 °C)). Scale bar for A is 10 s and for B 2 s

Fig. 5 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 5 Appearance of some taxa of Western Palaearctic Tettigonia (relative size proportions between photos not retained). a T. cantans, male, Germany, Gunzenhausen; b T. cantans, female, Germany, Gunzenhausen; c T. uvarovi Ebner, 1946—male, holotype, Siberia (NHMW), lateral view; d same, dorsal view; e T. caudata, male, Bulgaria, Russe district, Byala; f T. acutipennis Ebner, 1946—male, holotype, "Kleinasien 1914 | Marasch, Tölg. | coll. R. Ebner" (NHMW), dorsal view; g same, lateral view; h T. armeniaca, male, Armenia, Djermuk; i T. armeniaca, male, Turkey, Ispir; j T. viridissima morphotype of longealata, male, Morocco, El Kebab; k T. viridissima morphotype of longealata, female, Morocco, El Kebab; l T. viridissima morphotype of vaucheriana, male, Morocco, El Kebab; and m T. viridissima, male and female in copula, Bulgaria, Haskovo district, Kostilkovo village

Fig. 4 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 4 Phylogenetic tree of the genus Tettigonia based on BI analysis of concatenated COI-ITS1-ITS2 sequences. BI posterior probability (PP) values are shown near resolved branches (only support values above 0.50). Species groups, as defined by genetic and morpho-acoustic data, are distinctly shaded, and the respective branches are marked with an open circle and a capital letter as follows: "A"—T. viridissima group, "B"—T. caudata group, and "C"—T. cantans group. Haplotype codes correspond to Table 1 in the Supplement, followed by morphological identification. Squares on the right side of names correspond to relative wing length: filled squares short wings and open squares long wings;

Fig. 7 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 7 Relationship between the duration of chirps and inter-chirp intervals in T. caudata and the Tettigonia armeniaca complex. Green triangles mark recordings from Ispir, Turkey, where monosyllabic, disyllabic, and polysyllabic songs of T. armeniaca were recorded, as well as a song of T. caudata (Color figure online)

opencc-by-4.0Jan 2019View details →

Figure 2 in Acoustic documentation of temperate odontocetes in the Bering and Chukchi Seas

Figure 2. (A) Map of the acoustic detections attributed to northern right whale dolphins at sites M2 and M5 with dotted lines denoting historical range extents. (B) A spectrogram from 10 November 2012, at M2 of a downsweeping, harmonic click/buzz at 0.5 s with is characteristic of northern right whale dolphins (despite being masked by the ping of another instrument on the mooring). Spectrogram settings are: window = Hanning, Nfft = 512, overlap = 99%, min dB = 11 dB, dB spread = 35 dB.

opencc-by-4.0Jan 2019View details →

Figure 3 in Acoustic documentation of temperate odontocetes in the Bering and Chukchi Seas

Figure 3. (top) Map of acoustic detections attributed to Pacific white-sided dolphins at M2, M5, and CH with dotted lines denoting historical range extents. Number of files with detections each day are in parentheses if>1. (bottom) A spectrogram from 25 October 2009, at M2 of buzzes with peaks and notches as expected for Pacific white-sided dolphins according to Soldevilla et al. (2008). Spectrogram settings are: window = Hanning, Nfft = 1,024, overlap = 99%, min dB = 12 dB, dB spread = 42 dB.

opencc-by-4.0Jan 2019View details →

Figure 1 in Acoustic documentation of temperate odontocetes in the Bering and Chukchi Seas

Figure 1. (A) Map of acoustic detections attributed to Risso's dolphins at sites M2, M5, and CH with dotted lines denoting historical range extents. (B) A spectrogram from 31 March 2010, at M2 zoomed into clicks attributed to Risso's dolphins because peaks and notches are centered near the expected frequencies reported in Soldevilla et al. (2008). Spectrogram settings are: window = Hanning, Nfft = 2,048, overlap = 99%, min dB = 10 dB, dB spread = 40 dB.

Figure 3 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.

Figure 3. Percent of porpoise-positive minutes (PPM) that contained at least five click trains with minimum interclick intervals (MICIs) of <10 ms, thus classified as a buzz-positive minute (BPM). The star symbols and brackets represent post hoc Tukey tests that gave significant results at the P <0.05 level: Morning ťs. Day and Day ťs. Night for the offshore site.

Figure 2 in Variability in blue whale acoustic behavior off southern California

Figure 2. The percent of singular A (orange), singular B (blue), and D (green) call detections, and single (purple) and repetitive (maroon) AB phrase detections that occurred out of the total number of monthly detections at each of the four sites. Monthly detection totals are listed at the top of each bar. Periods shaded in gray indicate times when there was no data at that site.

Figure 3 in Variability in blue whale acoustic behavior off southern California

Figure 3. Residual plots of monthly variability for singular A (diamonds), singular B (squares), and D (circles) call detections (left panels), and single (upright triangles) and repetitive (downward triangles) AB phrase detections (right panels).

Figure 4 in Variability in blue whale acoustic behavior off southern California

Figure 4. Proportion of song consisting of each type per month at the inshore and offshore sites. Only months that contained identifiable song are included. No song was identified at the offshore sites in December 2009. Months when no data were available are marked in dark gray (Table 1).

Figure 4 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.

Figure 4. Distribution of harbor porpoise acoustic activity at the reef site measured as (a) porpoise positive minute (PPM) and (b) buzz positive minute (BPM) as a function of the tidal cycle. The length of the bars represents the binned presence of PPM or BPM during a given tidal phase. The black arrows represent the peak in mean PPMs and BPMs, respectively.

Figure 1 in Variability in blue whale acoustic behavior off southern California

Figure 1. Percent of all song bouts recorded at the inshore and offshore sites comprised of the two blue whale phrase types between September 2009 and August 2010.

Figure 2 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.

Figure 2. Percent of daily monitored minutes in which harbor porpoise were detected for the reef and offshore sites throughout the study period. The gray shaded areas represent data gaps between deployments.

Figure 1 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.

Figure 1. Bathymetric overview of study area in coastal Oregon (see inset) with acoustic instrumentation deployment sites displayed by the black dots.