Author(s): Xiongdong Zhou; Yibo Liu; Chubin Weng; Mengzhen Xu
Linked Author(s): Xiongdong Zhou, Mengzhen Xu
Keywords: Gymnocypris eckloni; Population recovery; Matrix population modeling; Artificial reproduction and release; Upper Yellow River
Abstract: The spotted naked carp (Gymnocypris eckloni), a representative species widely distributed in China’s upper Yellow River, has faced dramatic decline due to ecological pressures like overfishing and habitat fragmentation, resulting in a 90% population decrease from the 1960s to the 2000s. To counter this, the Chinese government has initiated an artificial reproduction and release program in the Longyangxia-Liujiaxia reach of the upper Yellow River since 2008. In study, we employed matrix projection population modeling to quantitatively assess the impact of this intervention on the spotted naked carp population. We compared population growth rates and elasticities from the onset of recovery in 2008 to eight years later in 2016 using equilibrium analysis on density-independent matrices. Additionally, a stochastic, density-dependent matrix was utilized to evaluate responses to varying levels of artificial reproduction and release (addition of age-0 fish from hatcheries). Our findings highlight that a swift population rebound in response to artificial reproduction and release, especially when initial population levels were low, and that if current reproduction efforts are maintained, the population biomass could attain 50%, 75%, and 100% of maximum carrying capacity within 25,45, and 75 years, respectively. Interestingly, our modeling suggests that gradually reducing reproduction releases, once the cumulative release quantity is controlled, may be a more effective strategy for population recovery than gradually increasing implementations. Overall, the artificial reproduction and release is a viable method for restoring the spotted naked carp population in the upper Yellow River, though optimizing release schedules is further needed to enhance recovery efficiency and minimize costs.
Year: 2024