Objective 3: Applications

Monitor, archive and analyze automated acoustic observations from a range of habitats associated with the KBS-LTER.

To monitor and characterize ecological acoustic signals at the Kellogg Biological Station LTER treatments, we selected 13 sites and classified them into two categories: agricultural (4 sites) and forested (9 sites). Agricultural sites consisted of two alfalfa plots and two poplar plots. Forested sites were composed of three coniferous (CF-1, 2, 3), three deciduous (DF-1, 2, 3), and three succession plots (SF-1, 2, 3). Acoustic signals within these sites were recorded for three days every two weeks, from May 18 to July 15, 2005. Twelve recordings were made per day in each plot, with each recording session lasting for 3 minutes. Each 3-minute recording was digitized and divided into 30-second acoustic samples for further analysis (Click to see more details).

Since the structure of acoustic signals is complex and includes many variables. We utilized Principal Components Analysis (PCA). This allowed us to determine which acoustic frequency band was highly associated with the intensity of either biological or anthropogenic (human-made) sounds. Figure 1 shows that acoustic signals vary at different times and in different habitat types.

Figure 1. The positive values of PC3 scores consist mainly of biological signals (3 to 6 kHz). The negative values contain anthropogenic sounds (1 to 2 kHz). The temporal pattern of ecological sound intensity is shown on the left and the variability of biological acoustic intensity is shown on the right. The first letter of the location labels refers to one of five different habitats (A=Alfalfa fields, C=Coniferous forests, D=Deciduous forests, P=Poplar stands, S=Succession). The second letter of the location labels refers to one of three replicates. Means +/- s.e.

Lists of species occurrence and number of calls made by each species in each site were compiled (Figure 2), thereby providing an indication of biological acoustic diversity. We were then able to characterize each habitat sampled in terms of the dominant bird species (Table 1). In addition, acoustic species diversity was determined using Shannon-Wiener index (Figure3).

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Figure 2. Avian species were identified by listening to the recordings. The number of bird calls identified is shown on the left and the number of bird species identified is shown on the right.

Table 1. The dominant bird species identified from the automated recordings from five different ecosystems. Forty three avian species were identifed from acoustic recordings.

Figure 3. Avian species diversity was determined using Shannon-Wiener Index with the number of vocalizations and species. The positive relationship between avian species richness and the number of calls is shown on the left, and the positive relationship between acoustic species diversity and biological acoustic intensity is shown on the right.

 

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