Palamara Gian Marco, Carrara Francesco, Smith Matthew J, Petchey Owen L
Department of Evolutionary Biology and Environmental Studies University of Zurich Zürich Switzerland; Computational Science Laboratory Microsoft Research Cambridge UK.
Ralph, M. Parson Laboratory, Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA; Department of Civil, Environmental and Geomatic Engineering ETH Zürich Switzerland.
Ecol Evol. 2016 Oct 27;6(23):8440-8451. doi: 10.1002/ece3.2495. eCollection 2016 Dec.
Invasive species are a serious threat to biodiversity worldwide and predicting whether an introduced species will first establish and then become invasive can be useful to preserve ecosystem services. Establishment is influenced by multiple factors, such as the interactions between the introduced individuals and the resident community, and demographic and environmental stochasticity. Field observations are often incomplete or biased. This, together with an imperfect knowledge of the ecological traits of the introduced species, makes the prediction of establishment challenging. Methods that consider the combined effects of these factors on our ability to predict the establishment of an introduced species are currently lacking. We develop an inference framework to assess the combined effects of demographic stochasticity and parameter uncertainty on our ability to predict the probability of establishment following the introduction of a small number of individuals. We find that even moderate levels of demographic stochasticity influence both the probability of establishment, and, crucially, our ability to correctly predict that probability. We also find that estimation of the demographic parameters of an introduced species is fundamental to obtain precise estimates of the interaction parameters. For typical values of demographic stochasticity, the drop in our ability to predict an establishment can be 30% when having priors on the demographic parameters compared to having their accurate values. The results from our study illustrate how demographic stochasticity may bias the prediction of the probability of establishment. Our method can be applied to estimate probability of establishment of introduced species in field scenarios, where time series data and prior information on the demographic traits of the introduced species are available.
入侵物种对全球生物多样性构成严重威胁，预测一个引入物种是否会首先定殖并随后成为入侵物种，对于保护生态系统服务可能会有所帮助。定殖受到多种因素的影响，例如引入个体与本地群落之间的相互作用，以及种群统计学和环境随机性。实地观察往往不完整或存在偏差。这一点，再加上对引入物种生态特征的了解不全面，使得定殖预测具有挑战性。目前缺乏考虑这些因素对我们预测引入物种定殖能力的综合影响的方法。我们开发了一个推理框架，以评估种群统计学随机性和参数不确定性对我们预测少量个体引入后定殖概率能力的综合影响。我们发现，即使是适度水平的种群统计学随机性，也会影响定殖概率，而且至关重要的是，会影响我们正确预测该概率的能力。我们还发现，估计引入物种的种群统计学参数对于获得相互作用参数的精确估计至关重要。对于典型的种群统计学随机性值，与拥有准确值相比，当对种群统计学参数有先验知识时，我们预测定殖的能力可能会下降30%。我们研究的结果说明了种群统计学随机性如何可能使定殖概率的预测产生偏差。我们的方法可应用于估计实地场景中引入物种的定殖概率，在这些场景中可获得时间序列数据和关于引入物种种群统计学特征的先验信息。
