Detecting population trends for US marine mammals

• Published in: Conservation Science and Practice
• Main Authors: White, Easton R., Schakner, Zachary, Bellamy, Amber, Srinivasan, Mridula
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Nature and Landscape Conservation; Environmental Science (miscellaneous); Ecology; Global and Planetary Change; Beluga; Beluga*; Delphinapterus leucas; Humpback Whale; Killer Whale; Megaptera novaeangliae; Orca; Orcinus orca; Killer whale
• Online Access: http://dx.doi.org/10.1111/csp2.611; https://onlinelibrary.wiley.com/doi/pdf/10.1111/csp2.611; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/csp2.611

Abstract Trend analysis can provide valuable information about marine mammal population dynamics, potentially revealing the influence of environmental factors and inform conservation and management decisions. We reviewed the marine mammal stock assessment reports (SARs) published by the US National Marine Fisheries Service and found that 80% of the selected 244 marine mammal stocks with SARs lack assessment for trends in population abundance. We compared trend analysis with another common management tool, potential biological removal (PBR), a measure of the maximum human‐caused mortality that can still result in positive population growth. We found that, generally, estimates of PBR were lower for declining stocks than for increasing or stable stocks and varied by life history characteristics. As a case study, we used a resampling approach on three well‐studied stocks, killer whale ( Orcinus orca —Northern Resident), beluga ( Delphinapterus leucas —Cook Inlet), and humpback whale ( Megaptera novaeangliae —CA/OR/WA), to test the minimal amount of time and sampling necessary to detect population trends with high statistical power. We found seven sampling events over more than 10 years were needed for a high statistical power level for all three stocks. Altogether, these findings suggest that well‐studied stocks can provide crucial information on the statistical requirements for detecting trends. Furthermore, our proposed resampling approach might enable more frequent trend analysis, even with limited time series available for many stocks.