Vocalizations of wild West Indian manatee vary across subspecies and geographic location

Reyes-Arias, Jessica D.; Brady, Beth; Ramos, Eric A.; Henaut, Yann; Castelblanco-Martínez, Delma Nataly; Maust-Mohl, Maria; Searle, Linda; Pérez-Lachaud, Gabriela; Guzmán, Héctor M.; Poveda, Héctor; Merchan, Fernando; Contreras, Kenji; Sanchez-Galan, Javier E.; Collom, Kristi A.; Magnasco, Marcelo O.

doi:10.1038/s41598-023-37882-8

Cited by 2 publications

(1 citation statement)

References 73 publications

(124 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their findings shed light on the existence of shared vocalizations between these groups and show a remarkable consistency in acoustic communication despite differences in habitats, genetic backgrounds, and geographical dispersion. In another study, Brady et al (2023) studied the vocalization patterns of manatees in two different habitats off St. George's Caye in Belize. Using recordings collected over 19 days, they analyzed 3,262 calls to examine differences in call rates, types and environmental influences between sites.…”

Section: Introductionmentioning

confidence: 99%

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

Schneider,

von Fersen,

Dierkes

2024

Front. Conserv. Sci.

View full text Add to dashboard Cite

The population sizes of manatees in many regions remain largely unknown, primarily due to the challenging nature of conducting visual counts in turbid and inaccessible aquatic environments. Passive acoustic monitoring has shown promise for monitoring manatees in the wild. In this study, we present an innovative approach that leverages a convolutional neural network (CNN) for the detection, isolation and classification of manatee vocalizations from long-term audio recordings. To improve the effectiveness of manatee call detection and classification, the CNN works in two phases. First, a long-term audio recording is divided into smaller windows of 0.5 seconds and a binary decision is made as to whether or not it contains a manatee call. Subsequently, these vocalizations are classified into distinct vocal classes (4 categories), allowing for the separation and analysis of signature calls (squeaks). Signature calls are further subjected to clustering techniques to distinguish the recorded individuals and estimate the population size. The CNN was trained and validated using audio recordings from three different zoological facilities with varying numbers of manatees. Three different clustering methods (community detection with two different classifiers and HDBSCAN) were tested for their suitability. The results demonstrate the ability of the CNN to accurately detect manatee vocalizations and effectively classify the different call categories. In addition, our study demonstrates the feasibility of reliable population size estimation using HDBSCAN as clustering method. The integration of CNN and clustering methods offers a promising way to assess manatee populations in visually challenging and inaccessible regions using autonomous acoustic recording devices. In addition, the ability to differentiate between call categories will allow for ongoing monitoring of important information such as stress, arousal, and calf presence, which will aid in the conservation and management of manatees in critical habitats.

show abstract

Section: Introductionmentioning

confidence: 99%

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

Schneider,

von Fersen,

Dierkes

2024

Front. Conserv. Sci.

View full text Add to dashboard Cite

show abstract

Female African elephant rumbles differ between populations and sympatric social groups

Pardo,

Lolchuragi,

Poole

et al. 2024

R. Soc. Open Sci.

View full text Add to dashboard Cite

Vocalizations often vary in structure within a species, from the individual to population level. Vocal differences among social groups and populations can provide insight into biological processes such as vocal learning and evolutionary divergence, with important conservation implications. As vocal learners of conservation concern, intraspecific vocal variation is of particular interest in elephants. We recorded calls from individuals in multiple, wild elephant social groups in two distinct Kenyan populations. We used machine learning to investigate vocal differentiation among individual callers, core groups, bond groups (collections of core groups) and populations. We found clear evidence for vocal distinctiveness at the individual and population level, and evidence for much subtler vocal differences among social groups. Social group membership was a better predictor of call similarity than genetic relatedness, suggesting that subtle vocal differences among social groups may be learned. Vocal divergence among populations and social groups has conservation implications for the effects of social disruption and translocation of elephants.

show abstract

Vocalizations of wild West Indian manatee vary across subspecies and geographic location

Cited by 2 publications

References 73 publications

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

Female African elephant rumbles differ between populations and sympatric social groups

Contact Info

Product

Resources

About