【摘要】 对十多年来国外植物 PVA的研究进行了综合评述 :具体分析了影响植物种群生存力的各种随机性因子及确定性因子 ;总结了植物 PVA研究的方法步骤及采用的模拟模型 ;探讨了植物 PVA的难点、PVA对管理措施的评价效果 ;并提出对今后植物 PVA的研究展望。认为 PVA是研究濒危植物种群灭绝及评价管理或保护措施的有力工具 ;发展描述复杂种间关系的多物种的 PVA模型以及包含多个影响因子的 PVA应用模型是未来植物 PVA的研究方向。更多还原

【Abstract】 Studies on plant population viability analysis(PVA) since 1990s have been reviewed in this paper, which discusses: factors affecting plant population viability; methods and steps of plant PVA; plant PVA models; and challenges to plant PVAs. In addition, the accuracy and effectiveness of plant PVAs in management or conservation strategies, as well as their limitations and future development have been discussed. The factors affecting plant population viability can be divided into stochastic factors and deterministic factors. Stochastic factors include environmental stochasticity, demographic stochasticity, genetic stochasticity and natural catastrophes. Environmental stochasticity is the most common stochastic factor being incorporated into plant PVA models. Demographic stochasticity and natural catastrophes have been simulated in some plant PVA models. Though genetic stochasticity rarely appears in PVA models, it is an important factor in long term. Different from animals, a plant population is always associated with certain type of community, and influenced by some deterministic factors such as interspecific competitions, herbivores, mutualisms. For endangered plant populations, the loss or deterioration of habitat can be a fatal deterministic factor, which is irreversible. Methods and steps of plant PVA have been summarized in this paper, based on published plant PVAs. First step is the assessment of population growth trajectories, and identification of life history stages. Life history stages of a population can be classified into seed, seedling, juvenile and adult. Second step is to record demographic parameters, and measure and estimate stochastic factors. In third step, a population dynamic model will be built on project matrices. Then, the model is parameterized with transition matrices. In following step, the correlation of parameters is analyzed, the effect of factors is examined, and the model is developed. Finally, the model is simulated on computer. Results will be persistence probability of population and minimum viable population at a given time. Stochastic simulation models, and metapopulation dynamics models have been found in plant PVAs. They are built on transition matrices, and different from animal Vortex model based on Monte Carlo simulations. The stochastic simulation model, incorporating stochastic factors, is often applied in a single population, and considered a valuable method. The metapopulation dynamics model, which is mainly used in populations with turnovers, has great potential for plant PVA. Some plant characters are different from those of animals. Unique plant characters include seed dormancy, diverse mating systems, periodic recruitment, clonal growth and so on. All these characters make it difficult to survey demographic parameters, which are indispensable for plant PVA. Besides predicting extinction time and extinction probability of a population over a given period, PVA has been applied in assessing the effectiveness of management and conservation strategies, which are intended to maximize population viability, and set the size of targeted plant conservation area. Another result of PVA is a minimum viable population (MVP). In conclusion, PVA is a useful tool to estimate extinction probability, and evaluate management or conservation strategies. Developing more practical models and methods of plant PVAs for resource managers is also an urgent task of biologists and modelers. Future plant PVAs should develop models incorporating multi species and various factors, and emphasize influences of whole community, ecosystem, landscape and region.更多还原