AUTHOR=Hammond Philip S. , Francis Tessa B. , Heinemann Dennis , Long Kristy J. , Moore Jeffrey E. , Punt André E. , Reeves Randall R. , Sepúlveda Maritza , Sigurðsson Guðjón Már , Siple Margaret C. , Víkingsson Gísli , Wade Paul R. , Williams Rob , Zerbini Alexandre N. TITLE=Estimating the Abundance of Marine Mammal Populations JOURNAL=Frontiers in Marine Science VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2021.735770 DOI=10.3389/fmars.2021.735770 ISSN=2296-7745 ABSTRACT=

Motivated by the need to estimate the abundance of marine mammal populations to inform conservation assessments, especially relating to fishery bycatch, this paper provides background on abundance estimation and reviews the various methods available for pinnipeds, cetaceans and sirenians. We first give an “entry-level” introduction to abundance estimation, including fundamental concepts and the importance of recognizing sources of bias and obtaining a measure of precision. Each of the primary methods available to estimate abundance of marine mammals is then described, including data collection and analysis, common challenges in implementation, and the assumptions made, violation of which can lead to bias. The main method for estimating pinniped abundance is extrapolation of counts of animals (pups or all-ages) on land or ice to the whole population. Cetacean and sirenian abundance is primarily estimated from transect surveys conducted from ships, small boats or aircraft. If individuals of a species can be recognized from natural markings, mark-recapture analysis of photo-identification data can be used to estimate the number of animals using the study area. Throughout, we cite example studies that illustrate the methods described. To estimate the abundance of a marine mammal population, key issues include: defining the population to be estimated, considering candidate methods based on strengths and weaknesses in relation to a range of logistical and practical issues, being aware of the resources required to collect and analyze the data, and understanding the assumptions made. We conclude with a discussion of some practical issues, given the various challenges that arise during implementation.