Dear colleagues, My co-authors and I are pleased to announce the publication of our article entitled « *Potential signature whistle production by Indo-Pacific humpback dolphins, Sousa chinensis, in the northern South China Sea » *in Animal Behaviour. Abstract: Dolphin communication involves acoustic signals, including whistles, and the well-studied bottlenose dolphins produce individually distinctive whistles called signature whistles (SWs). The production of a potential SW by an injured Indo-Pacific humpback dolphin has been reported, but no study has attempted to validate this finding in this species. Using data collected during encounters with free-ranging Indo-Pacific humpback dolphins at two locations in the northern South China Sea, we investigated the production of SWs by these dolphins. Of the 3846 analysed whistles, 37% were identified as potential SWs (PSWs) using the SIGnature whistle IDentification method and categorized into 82 PSW types. Overall, PSWs were identified during 54% of encounters. Given the high production rate of stereotyped whistles (62% of all whistles in 90% of encounters) compared with the identified PSWs, we suggest that the SIGnature whistle IDentification method criteria cannot be fully adapted for the detection of SWs in Indo-Pacific humpback dolphins, and more research should be conducted to adapt the criteria to the species. In addition, the characteristics of PSWs differed slightly between locations, potentially because of the geographical separation of populations and habitat differences (e.g. noise levels). The present results confirm the production of stereotyped whistles, including PSWs, by Indo-Pacific humpback dolphins. Further research should be conducted to confirm whether these whistles are similar to bottlenose dolphins’ SWs. The article is freely accessible via this link: https://kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F%2Fauthors.elsevier.com... For any queries, please feel free to contact me at agathe@idsse.ac.cn or agathe.serres11@gmail.com Best, Agathe On Fri 1 Nov 2024 at 22:30, <marmam-request@lists.uvic.ca> wrote:
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Today's Topics:
1. `Xcertainty` R package now available! Incorporating uncertainty associated with drone-based photogrammetry of marine mammals (Bierlich, Kevin C) 2. Job Opening: Wildlife Biologist at Alaska Dept. of Fish and Game (Pearson, Linnea E (DFG)) 3. New publication: killer whale predation in the SW Indian Ocean (Maeva Terrapon) 4. New publication: Stress and Reproductive Hormones of Free-Ranging Dolphins Across a Natural Salinity Gradient (Guinn, Makayla)
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Message: 1 Date: Fri, 1 Nov 2024 02:41:09 +0000 From: "Bierlich, Kevin C" <kevin.bierlich@oregonstate.edu> To: "marmam@lists.uvic.ca" <marmam@lists.uvic.ca> Subject: [MARMAM] `Xcertainty` R package now available! Incorporating uncertainty associated with drone-based photogrammetry of marine mammals Message-ID: < CO1P222MB01139F1EEF034C16B2A308A78A562@CO1P222MB0113.NAMP222.PROD.OUTLOOK.COM
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Hey MarMam!
We are excited to announce that the Xcertainty R package is now available on CRAN!
Xcertainty is an easy-to-use R package that uses a Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements of marine mammals derived from drones.
The easiest way to install Xcertainty is via CRAN:
install.packages("Xcertainty") library(Xcertainty)
Learn more: GitHub: https://github.com/MMI-CODEX/Xcertainty CODEX website: https://mmi.oregonstate.edu/centers-excellence/codex/software-hardware/xcert... CRAN: https://cran.r-project.org/web/packages/Xcertainty/index.html All morphological measurements derived using drone-based photogrammetry are susceptible to uncertainty. This uncertainty often varies by the drone system used. Thus, it is critical to incorporate photogrammetric uncertainty associated with measurements collected using different drones so that results are robust and comparable across studies and over long-term datasets. The Xcertainty R package makes this simple and easy by producing a predictive posterior distribution for each measurement. This posterior distribution can be summarized to describe the measurement (i.e., mean, median) and its associated uncertainty (i.e., standard deviation, credible intervals). The posterior distributions are also useful for making probabilistic statements, such as classifying maturity or diagnosing pregnancy if a proportion of the posterior distribution for a given measurement is greater than a specified threshold (e.g., if greater than 50% of posterior distribution for total body length is > 10 m, the individual is classified as mature). Xcertainty is based off of previously published Bayesian statistical models. In essence, measurements of known-sized objects (?calibration objects?) collected at various altitudes are used as training data to predict morphological measurements (e.g., body length) and associated uncertainty of unknown-sized objects (e.g., whales). Xcertainty also includes functions that incorporate multiple measurements (body length and width) to estimate different body condition metrics (i.e., single widths, surface area, body volume, body area index) with associated uncertainty, as well as combine body length with age information to construct growth curves Cheers, KC Bierlich & Josh Hewitt
KC (Kevin) Bierlich, PhD, MEM Assistant Professor Senior Research Center of Drone Excellence (CODEX< https://mmi.oregonstate.edu/centers-excellence/codex>) Marine Mammal Institute, Dept. of Fisheries, Wildlife, & Conservation Sciences, Oregon State University Pronouns: he, him, his kevin.bierlich@oregonstate.edu<mailto:kevin.bierlich@oregonstate.edu>