Acoustic recordings provide detailed information regarding the behavior of cryptic wildlife to support conservation translocations

Listening to animal behavior to understand changing ecosystems

Interpreting sound gives powerful insight into the health of ecosystems. Beyond detecting the presence of wildlife, bioacoustic signals can reveal their behavior. However, behavioral bioacoustic information is underused because identifying the function and context of animals' sounds remains challenging. A growing acoustic toolbox is allowing researchers to begin decoding bioacoustic signals by linking individual and population-level sensing. Yet, studies integrating acoustic tools for behavioral insight across levels of biological organization remain scarce. We aim to catalyze the emerging field of behavioral bioacoustics by synthesizing recent successes and rising analytical, logistical, and ethical challenges. Because behavior typically represents animals' first response to environmental change, we posit that behavioral bioacoustics will provide theoretical and applied insights into animals' adaptations to global change.

Toward Passive Acoustic Monitoring of Lemurs: Using an Affordable Open-Source System to Monitor Phaner Vocal Activity and Density

Developing new cost-effective methods for monitoring the distribution and abundance of species is essential for conservation biology. Passive acoustic monitoring (PAM) has long been used in marine mammals and has recently been postulated to be a promising method to improve monitoring of terrestrial wildlife as well. Because Madagascar’s lemurs are among the globally most threatened taxa, this study was designed to assess the applicability of an affordable and open-source PAM device to estimate the density of pale fork-marked lemurs (Phaner pallescens). Using 12 playback experiments and one fixed transect of four automated acoustic recorders during one night of the dry season in Kirindy Forest, we experimentally estimated the detection space for Phaner and other lemur vocalizations. Furthermore, we manually annotated more than 10,000 vocalizations of Phaner from a single location and used bout rates from previous studies to estimate density within the detection space. To truncate detections beyond 150 m, we applied a sound pressure level (SPL) threshold filtering out vocalizations below SPL 50 (dB re 20 μPa). During the dry season, vocalizations of Phaner can be detected with confidence beyond 150 m by a human listener. Within our fixed truncated detection area corresponding to an area of 0.07 km2 (detection radius of 150 m), we estimated 10.5 bouts per hour corresponding to a density of Phaner of 38.6 individuals/km2. Our density estimates are in line with previous estimates based on individually marked animals conducted in the same area. Our findings suggest that PAM also could be combined with distance sampling methods to estimate densities. We conclude that PAM is a promising method to improve the monitoring and conservation of Phaner and many other vocally active primates.

Analysis of the vaginal microbiome of giant pandas using metagenomics sequencing

In this study, a total of 14 vaginal samples (GPV1‐14) from giant pandas were analyzed. These vaginal samples were divided into two groups as per the region and age of giant pandas. All the vaginal samples were analyzed using metagenomic sequencing. As per the outcomes of metagenomic analysis, Proteobacteria (39.04%), Firmicutes (5.27%), Actinobacteria (2.94%), and Basidiomycota (2.77%) were found to be the dominant phyla in the microbiome of the vaginal samples. At the genus level, Pseudomonas (21.90%) was found to be the most dominant genus, followed by Streptococcus (3.47%), Psychrobacter (1.89%), and Proteus (1.38%). Metastats analysis of the microbial species in the vaginal samples of giant pandas from Wolong Nature Reserve, Dujiangyan and Ningbo Youngor Zoo, and Ya'an Bifengxia Nature Reserve was found to be significantly different (p < 0.05). Age groups, that is, AGE1 (5‐10 years old) and AGE2 (11‐16 years old), also demonstrated significantly different inter‐group microbial species (p < 0.05). For the first time, Chlamydia and Neisseria gonorrhoeae were detected in giant pandas’ reproductive tract. GPV3 vaginal sample (2.63%) showed highest Chlamydia content followed by GPV14 (0.91%), and GPV7 (0.62%). GPV5 vaginal sample (7.17%) showed the highest Neisseria gonorrhoeae content, followed by GPV14 (7.02%), and GPV8 (6.50%). Furthermore, we employed eggNOG, CAZy, KEGG, and NCBI databases to investigate the functional significance of giant panda's vaginal microbial community. The outcomes indicated that giant panda's vaginal microbes were involved in biological processes. The data from this study will help in improving the reproductive health of giant pandas.