Acoustic assessment of experimental reforestation in a Costa Rican rainforest
Effective forest restoration requires tools for evaluating and comparing restoration approaches. Nevertheless, measuring restoration progress can be difficult and expensive. Passive acoustic monitoring (PAM) can be an inexpensive assessment strategy to collect large amounts of biodiversity informati...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/382479775ad14fd29d6828293e4fb66d |
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| Sumario: | Effective forest restoration requires tools for evaluating and comparing restoration approaches. Nevertheless, measuring restoration progress can be difficult and expensive. Passive acoustic monitoring (PAM) can be an inexpensive assessment strategy to collect large amounts of biodiversity information at scale. Nevertheless, analyzing and interpreting this information remains a difficult challenge. In this study we applied and compared three approaches to assess restoration treatments using recordings collected from PAM. We tested the hypothesis that variation in forest structure translates into differences in the species composition and acoustic signature of sites. For this purpose we used a reforestation experiment on the Osa Peninsula of Costa Rica, where we compared the mature forest to four restoration treatments. The treatments included natural regeneration and three treatments that varied the ratio of balsa, a pioneer tree species, and other native species. Our first approach consisted of visual and acoustic review of recordings to describe taxonomic groups found in each location. Our second approach consisted of measuring the acoustic energy present in the 10–30 kHz frequency band, an acoustic range primarily occupied by the mating signals of katydids and other insects, important elements of the food web and are often less mobile than birds and mammals. In our third approach we created 24-hour spectrograms that represented sites and treatments. Using the 24-hour spectrograms, we calculated a PCA and used a tSNE to evaluate the differences in acoustic signature and visualize clusters of treatments. The first approach revealed that relying on visual and acoustic review would fail to find the diel acoustic patterns that were captured in the other two approaches. The approaches varied substantially in the amount of acoustic data incorporated and the amount of human processing time. Subsampling recordings demonstrated that using only 10 sec instead of 40 sec per recording generated comparable results. The failure to differentiate among restoration treatments could reflect insensitivity in the approaches, but more likely represents the fact that the restoration plots are newly established and that substantial differentiation is more likely to arise during the time course of restoration. |
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