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Estimating the rate of increase for the finless porpoise with special attention to predictions for the Inland Sea population in Japan
Authors:Midori Hashimoto  Kunio Shirakihara  Miki Shirakihara  Kazuhiko Hiramatsu
Affiliation:1. Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8564, Japan
2. Faculty of Science, Toho University, 2-2-1 Miyama, Funabashi, Chiba, 274-8510, Japan
Abstract:
The finless porpoise Neophocaena asiaeorientalis inhabits coastal waters and rivers in East Asia and is exposed to various human activities. This species is listed on the IUCN Red List of Threatened Species due to a reduction in abundance. Although human-induced mortality can be a threat to porpoise populations, future anthropogenic impacts have not been quantitatively evaluated due to lack of demographic information. Adequate future population projections are needed to form the basis for conservation measures before the population declines to critical levels. We conducted a population viability analysis for the population of finless porpoise in the Inland Sea, Japan using a Leslie matrix model composed of age-specific survival and fertility rates. We described the uncertainty in the annual rate of increase (λ) for the finless porpoise using randomly sampled estimates of survival rate for other cetaceans with similar life histories. Plausible median estimates of λ ranged from 1.041 (age at first reproduction [AFR] = 7) to 1.056 (AFR = 5). Future population changes and extinction probabilities were predicted after combining these estimates with a predicted human-induced mortality rate (M) and available abundance estimates. The extinction probability after 100 years was 0 %. However, the probability of the quasi-extinction (<100 individuals) was as high as 79.0 % after 100 years. The results also suggest that the persistence of the finless porpoise population could be achieved with a small effort to reduce anthropogenic mortality.
Keywords:Bayesian analysis  Extinction risk  Leslie matrix model  Monte Carlo methods  Population viability analysis
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