种群生存力分析（PVA）的方法与应用

随着人们对资源的加速利用,生境丧失和破碎化导致物种濒危问题日益严重.以岛屿生物地理学为理论起源的种群生存力分析（PVA）,通过分析和模拟种群动态过程并建立灭绝概率与种群数量之间的关系,为濒危物种保护提供了重要的理论依据和研究途径.在过去的几十年中,种群生存力分析已成为保护生物学中一项重要的研究内容.目前种群生存力分析发展稳定,但对其实际预测能力和准确性尚存质疑,应用方面也有待进一步发展.种群生存力分析的进一步完善还需要在理论和方法上的创新,特别是籍于景观生态学和可持续性科学的理念,将空间分析手段、经济社会因素纳入到物种和种群的预测和管理上,从而使其 具有更完整的理论基础和更高的实用价值.为此,本文对种群生存力分析的历史、基本概念、研究方法、模型应用和准确性进行了综述,并提出了有关的研究展望.     

When does parameter drift decrease the uncertainty in extinction risk estimates?

Halley (2003) proposed that parameter drift decreases the uncertainty in long‐range extinction risk estimates, because drift mitigates the extreme sensitivity of estimated risk to estimated mean growth rate. However, parameter drift has a second, opposing effect: it increases the uncertainty in parameter estimates from a given data set. When both effects are taken into account, parameter drift can increase, sometimes substantially, the uncertainty in risk estimates. The net effect depends sensitively on the type of drift and on which model parameters must be estimated from observational data on the population at risk. In general, unless many parameters are estimated from independent data, parameter drift increases the uncertainty in extinction risk. These findings suggest that more mechanistic PVA models, using long‐term data on key environmental variables and experiments to quantify their demographic impacts, offer the best prospects for escaping the high data requirements when extinction risk is estimated from observational data.     

A method for validating stochastic models of population viability: a case study of the mountain pygmy-possum (Burramys parvus)

1.  A method of validating stochastic models of population viability is proposed, based on assessing the mean and variance of the predicted population size.
2.  The method is illustrated with a model of the population dynamics of the mountain pygmy-possum ( Burramys parvus Broom 1895), based on annual census data collected from a single population in the Snowy Mountains of New South Wales, Australia between 1986 and 1997. The model incorporates density-dependence in survivorship and recruitment, and demographic and environmental stochasticity.
3.  The model appeared to make reasonable predictions for the three populations that were used for validation, provided the equilibrium population size was estimated accurately. This may require that differences in habitat quality between populations be taken into account.
4.  Following validation, the model was given new parameters using the additional data from the three populations, and the risk of population decline within the next 100 years was assessed. Although populations as small as 15 females are predicted to be relatively safe from extinction caused by stochastic processes, B. parvus appears vulnerable to loss of habitat and reductions in the population growth rate.
5.  The approach used in this paper is one of few attempts to validate a model of population viability using field data, and demonstrates that some aspects of stochastic population models can be tested.     

Poor environmental tracking can make extinction risk insensitive to the colour of environmental noise

The relative importance of environmental colour for extinction risk compared with other aspects of environmental noise (mean and interannual variability) is poorly understood. Such knowledge is currently relevant, as climate change can cause the mean, variability and temporal autocorrelation of environmental variables to change. Here, we predict that the extinction risk of a shorebird population increases with the colour of a key environmental variable: winter temperature. However, the effect is weak compared with the impact of changes in the mean and interannual variability of temperature. Extinction risk was largely insensitive to noise colour, because demographic rates are poor in tracking the colour of the environment. We show that three mechanisms-which probably act in many species-can cause poor environmental tracking: (i) demographic rates that depend nonlinearly on environmental variables filter the noise colour, (ii) demographic rates typically depend on several environmental signals that do not change colour synchronously, and (iii) demographic stochasticity whitens the colour of demographic rates at low population size. We argue that the common practice of assuming perfect environmental tracking may result in overemphasizing the importance of noise colour for extinction risk. Consequently, ignoring environmental autocorrelation in population viability analysis could be less problematic than generally thought.     

Population dynamics can be more important than physiological limits for determining range shifts under climate change

Evidence is accumulating that species' responses to climate changes are best predicted by modelling the interaction of physiological limits, biotic processes and the effects of dispersal‐limitation. Using commercially harvested blacklip (Haliotis rubra) and greenlip abalone (Haliotis laevigata) as case studies, we determine the relative importance of accounting for interactions among physiology, metapopulation dynamics and exploitation in predictions of range (geographical occupancy) and abundance (spatially explicit density) under various climate change scenarios. Traditional correlative ecological niche models (ENM) predict that climate change will benefit the commercial exploitation of abalone by promoting increased abundances without any reduction in range size. However, models that account simultaneously for demographic processes and physiological responses to climate‐related factors result in future (and present) estimates of area of occupancy (AOO) and abundance that differ from those generated by ENMs alone. Range expansion and population growth are unlikely for blacklip abalone because of important interactions between climate‐dependent mortality and metapopulation processes; in contrast, greenlip abalone should increase in abundance despite a contraction in AOO. The strongly non‐linear relationship between abalone population size and AOO has important ramifications for the use of ENM predictions that rely on metrics describing change in habitat area as proxies for extinction risk. These results show that predicting species' responses to climate change often require physiological information to understand climatic range determinants, and a metapopulation model that can make full use of this data to more realistically account for processes such as local extirpation, demographic rescue, source‐sink dynamics and dispersal‐limitation.     

Community extinction patterns in coloured environments

Understanding community responses to environmental variation is a fundamental aspect of ecological research, with direct ecological, conservation and economic implications. Here, we examined the role of the magnitude, correlation and autocorrelation structures of environmental variation on species' extinction risk (ER), and the probability of actual extinction events in model competitive communities. Both ER and probability increased with increasing positive autocorrelation when species responded independently to the environment, yet both decreased with a strong correlation between species-specific responses. These results are framed in terms of the synchrony between--and magnitude of variation within--species population sizes and are explained in terms of differences in noise amplification under different conditions. The simulation results are robust to changes in the strength of interspecific density dependence, and whether noise affects density-independent or density-dependent population processes. Similar patterns arose under different ranges of noise severity when these different model assumptions were examined. We compared our results with those from an analytically derived solution, which failed to capture many features of the simulation results.     

Spatially-correlated extinction in a metapopulation model of Leadbeater's Possum

The importance of considering spatially-correlated extinction in metapopulation viability analyses was investigated using a model of the population dynamics of Gymnobelideus leadbeateri McCoy (Leadbeater's Possum). Fire caused local extinction of G. leadbeateri and induced changes in the suitability of the habitat over a period of decades and centuries. Spatially-correlated fires, in which the correlation between the incidence of fire declines with distance, and uniformly-correlated fires were simulated. The predicted risk of metapopulation extinction increased: (i) as the variance in the number of fires each year increased, (ii) as the mean fire interval decreased, and (iii) as the mean dispersal distance decreased. Incorporating spatial correlation in the incidence of fires between patches had little effect on the results, provided the variance in the number of fires per year remained the same and fires modified habitat quality. The predicted risk of metapopulation extinction was greater for spatially-correlated fires than for uniformly-correlated fires when fires only caused local extinction but did not change habitat suitability. Incorporating spatial correlation in the incidence of fire within patches, which allowed partial burning of patches, reduced the predicted risk of extinction. This effect was only slight when patches were smaller than about 50 ha. The results of our simulations demonstrate the importance of considering correlations in disturbance regimes in metapopulation models, especially if these models are used to assist the design of nature reserves.     

Predicted population trends for Cozumel Curassows (Crax rubra griscomi): empirical evidence and predictive models in the face of climate change

ABSTRACT We assessed the current conservation status and population trends of Cozumel Curassows (Crax rubra griscomi), a critically endangered and endemic cracid of Cozumel Island, Mexico. Distance sampling was used to estimate the population size in 2005 and compare it with a previous estimate in 1995. Population viability analysis (PVA) was used to model population trends, considering ecological and environmental conditions prevailing in 1995 and 2005. Additional PVA models were constructed using different hypothetical scenarios to assess the effects of particular factors and management strategies on population trends. We estimated a population size of 372 ± 155 curassows in the island's tropical semideciduous forest, before two hurricanes hit Cozumel Island in 2005. PVA modeling predicted a steep decline in population size in the ensuing decades as a result of an increased frequency and intensity of hurricanes related to global climate change, and an observed female bias in the sex ratio likely caused by a higher mortality rate for adult males. We recommend urgent management actions for the long‐term conservation of Cozumel Curassows, including a ban on hunting, eradication of the feral fauna, particularly dogs, and implementation of a captive‐breeding program to supplement the wild population.     

Linear analysis solves two puzzles in population dynamics: the route to extinction and extinction in coloured environments

In this paper, we give simple explanations to two unsolved puzzles that have emerged in recent theoretical studies in population dynamics. First, the tendency of some model populations to go extinct from high population densities, and second, the positive effect of autocorrelated environments on extinction risks for some model populations. Both phenomena are given general explanations by simple, linear, sto-chastic models. We emphasize the predictive and explanatory power of such models.     

Integral projection models perform better for small demographic data sets than matrix population models: a case study of two perennial herbs

1. Matrix population models are widely used to describe population dynamics, conduct population viability analyses and derive management recommendations for plant populations. For endangered or invasive species, management decisions are often based on small demographic data sets. Hence, there is a need for population models which accurately assess population performance from such small data sets.
2. We used demographic data on two perennial herbs with different life histories to compare the accuracy and precision of the traditional matrix population model and the recently developed integral projection model (IPM) in relation to the amount of data.
3. For large data sets both matrix models and IPMs produced identical estimates of population growth rate (λ). However, for small data sets containing fewer than 300 individuals, IPMs often produced smaller bias and variance for λ than matrix models despite different matrix structures and sampling techniques used to construct the matrix population models.
4. Synthesis and applications . Our results suggest that the smaller bias and variance of λ estimates make IPMs preferable to matrix population models for small demographic data sets with a few hundred individuals. These results are likely to be applicable to a wide range of herbaceous, perennial plant species where demographic fate can be modelled as a function of a continuous state variable such as size. We recommend the use of IPMs to assess population performance and management strategies particularly for endangered or invasive perennial herbs where little demographic data are available.     

种群生存力分析:准确性和保护应用

目前已提出了五类估计濒危物种绝灭风险的种群生存力分析模型 ,即 :分析模型、单种群确定性模型、单种群随机模型、异质种群模型和显空间模型。模型的选择取决于物种的生活史特征和可用的数据。与用于保护实践的其他方法相比 ,种群生存力分析 (PVA)是相对准确的量化工具。然而 ,一些濒危物种种群统计学数据质量差和种群动态的有关假说模糊不清可能影响到模型预测的准确性 ,因此 ,要谨慎地使用PVA。在西方国家 ,PVA在濒危物种保护计划和管理中应用越来越广泛。它主要用于 :( 1)预测濒危物种未来的种群大小 ;( 2 )估计一定时间内物种的绝灭风险 ;( 3 )评估一套保护措施 ,确定哪个能使种群的存活时间最长 ;( 4)探索不同假说对小种群动态的影响 ;( 5 )指导濒危动物野外数据的搜集工作。我国的濒危物种很多 ,然而开展PVA研究的濒危物种却很少。应大力发展适合于模拟我国特有濒危物种及其保护问题的PVA模型     

Predictions of Taylor's power law, density dependence and pink noise from a neutrally modeled time series

There has recently been increasing interest in neutral models of biodiversity and their ability to reproduce the patterns observed in nature, such as species abundance distributions. Here we investigate the ability of a neutral model to predict phenomena observed in single-population time series, a study complementary to most existing work that concentrates on snapshots in time of the whole community. We consider tests for density dependence, the dominant frequencies of population fluctuation (spectral density) and a relationship between the mean and variance of a fluctuating population (Taylor's power law). We simulated an archipelago model of a set of interconnected local communities with variable mortality rate, migration rate, speciation rate, size of local community and number of local communities. Our spectral analysis showed ‘pink noise’: a departure from a standard random walk dynamics in favor of the higher frequency fluctuations which is partly consistent with empirical data. We detected density dependence in local community time series but not in metacommunity time series. The slope of the Taylor's power law in the model was similar to the slopes observed in natural populations, but the fit to the power law was worse. Our observations of pink noise and density dependence can be attributed to the presence of an upper limit to community sizes and to the effect of migration which distorts temporal autocorrelation in local time series. We conclude that some of the phenomena observed in natural time series can emerge from neutral processes, as a result of random zero-sum birth, death and migration. This suggests the neutral model would be a parsimonious null model for future studies of time series data.     

Modeling density dependence and climatic disturbances in caribou: a case study from the Bathurst Island complex, Canadian High Arctic

Peary caribou Rangifer tarandus pearyi is the northernmost subspecies of Rangifer in North America and endemic to the Canadian High Arctic. Because of severe population declines following years of unfavorable winter weather with ice coating on the ground or thicker snow cover, it is believed that density-independent disturbance events are the primary driver for Peary caribou population dynamics. However, it is unclear to what extent density dependence may affect population dynamics of this species. Here, we test for different levels of density dependence in a stochastic, single-stage population model, based on available empirical information for the Bathurst Island complex (BIC) population in the Canadian High Arctic. We compare predicted densities with observed densities during 1961–2001 under various assumptions of the strength of density dependence. On the basis of our model, we found that scenarios with no or very low density dependence led to population densities far above observed densities. For average observed disturbance regimes, a carrying capacity of 0.1 caribou km⁻² generated an average caribou density similar to that estimated for the BIC population over the past four decades. With our model we also tested the potential effects of climate change-related increases in the probability and severity of disturbance years, that is unusually poor winter conditions. On the basis of our simulation results, we found that, in particular, potential increases in disturbance severity (as opposed to disturbance frequency) may pose a considerable threat to the persistence of this species.     

Stochastic matrix models for conservation and management: a comparative review of methods

Stochastic matrix models are frequently used by conservation biologists to measure the viability of species and to explore various management actions. Models are typically parameterized using two or more sets of estimated transition rates between age/size/stage classes. While standard methods exist for analyzing a single set of transition rates, a variety of methods have been employed to analyze multiple sets of transition rates. We review applications of stochastic matrix models to problems in conservation and use simulation studies to compare the performance of different analytic methods currently in use. We find that model conclusions are likely to be robust to the choice of parametric distribution used to model vital rate fluctuations over time. However, conclusions can be highly sensitive to the within-year correlation structure among vital rates, and therefore we suggest using analytical methods that provide a means of conducting a sensitivity analysis with respect to correlation parameters. Our simulation results also suggest that the precision of population viability estimates can be improved by using matrix models that incorporate environmental covariates in conjunction with experiments to estimate transition rates under a range of environmental conditions.     

Intramolecular hydrogen bonding in articaine can be related to superior bone tissue penetration: a molecular dynamics study

Local anesthetics (LAs) are drugs that cause reversible loss of nociception during surgical procedures. Articaine is a commonly used LA in dentistry that has proven to be exceptionally effective in penetrating bone tissue and induce anesthesia on posterior teeth in maxilla and mandibula. In the present study, our aim was to gain a deeper understanding of the penetration of articaine through biological membranes by studying the interactions of articaine with a phospholipid membrane. Our approach involves Langmuir monolayer experiments combined with molecular dynamics simulations. Membrane permeability of LAs can be modulated by pH due to a titratable amine group with a pKa value close to physiological pH. A change in protonation state is thus known to act as a lipophilicity switch in LAs. Our study shows that articaine has an additional unique lipophilicity switch in its ability to form an intramolecular hydrogen bond. We suggest this intramolecular hydrogen bond as a novel and additional solvent-dependent mechanism for modulation of lipophilicity of articaine which may enhance its diffusion through membranes and connective tissue.