Siberian crane (Leucogeranus leucogeranus) is one of the most endangered species in the world. The ecological integrity of its main wintering ground at Poyang Lake in China is crucial for the future of the species because Poyang Lake accommodates 99% of its global population. With the Three Gorges Dam fully operational, science-based adaptive strategies are urgently needed to avoid catastrophic ecological consequences. This study quantified the link between water level variation and population growth rate of the Siberian crane in Poyang Lake using a suite of advanced statistical techniques. We first used the stochastic Gompert growth model within the state space modelling (SSM) framework to infer population growth rate, density dependence, and process variability and observation errors. We then applied generalized additive models (GAMs) to the population growth rate to quantify the effects of environmental stochasticity. Our SSM results indicated that there was little support for density dependence, and environmental stochasticity was the main forcing for Siberian crane population variations in Poyang Lake. Although the SSM suggested that water levels in both high- and low-water seasons were important factors for Siberian crane population, inference on their effects were elusive because of large confidence intervals of the estimated coefficients. Using GAM, we confirmed the non-linear effects of water level on population growth rate. Based on the modelled response curves, we proposed the optimal water level for Siberian crane conservation: (a) maximum summer water season level should be less than 19.5m and (b) minimum winter water level should be between 8.7-10.2m. Our methods of integrating population dynamic model and GAM have wide relevance for regional biological conversation efforts that seek to maintain a resilient population of threatened species.
- Feneralized additive model (GAM)
- Population dynamics water level
- Siberian crane
- State space model (SSM)
- Three Gorges Dam