%0 Journal Article
%T Advances in Plant Population Viability Analysis<br>植物种群生存力分析研究进展
%A PENG Shao Lin
%A <br>彭少麟
%J 生态学报
%D 2002
%I 
%X 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 effect
%K population viability analysis
%K plant
%K PVA models
%K minimum viable population<br>植物
%K 种群生存力分析
%K 研究进展
%K PVA模型
%K 最小可存活种群
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=FE163E5DB2274E5937319DE98913EC37&aid=9BABD8832AF7EB03&yid=C3ACC247184A22C1&vid=BC12EA701C895178&iid=59906B3B2830C2C5&sid=FBEA8C467B1EDA46&eid=6706B133F8421241&journal_id=1000-0933&journal_name=生态学报&referenced_num=5&reference_num=78