Centre for Invasion Biology, Department of Mathematical Sciences, Stellenbosch University, Matieland 7602, South Africa.
Mathematical and Physical Biosciences, African Institute for Mathematical Sciences, Cape Town 7945, South Africa.
Proc Natl Acad Sci U S A. 2017 Nov 21;114(47):12507-12511. doi: 10.1073/pnas.1704213114. Epub 2017 Nov 6.
Population demography is central to fundamental ecology and for predicting range shifts, decline of threatened species, and spread of invasive organisms. There is a mismatch between most demographic work, carried out on few populations and at local scales, and the need to predict dynamics at landscape and regional scales. Inspired by concepts from landscape ecology and Markowitz's portfolio theory, we develop a landscape portfolio platform to quantify and predict the behavior of multiple populations, scaling up the expectation and variance of the dynamics of an ensemble of populations. We illustrate this framework using a 35-y time series on gypsy moth populations. We demonstrate the demography accumulation curve in which the collective growth of the ensemble depends on the number of local populations included, highlighting a minimum but adequate number of populations for both regional-scale persistence and cross-scale inference. The attainable set of landscape portfolios further suggests tools for regional population management for both threatened and invasive species.
人口统计学是基础生态学的核心，对于预测物种分布范围的变化、受威胁物种的减少以及入侵生物的传播具有重要意义。大多数人口统计学研究都是在少数几个种群和局部范围内进行的，而这与在景观和区域尺度上预测动态的需求之间存在不匹配。受景观生态学和马科维茨投资组合理论的启发，我们开发了一个景观投资组合平台，用于量化和预测多个种群的行为，从而扩大了种群集合动态的期望和方差。我们使用 35 年的舞毒蛾种群时间序列来说明这一框架。我们展示了人口统计学积累曲线，其中集合的集体增长取决于所包含的局部种群数量，突出了一个最小但足够数量的种群，以便在区域尺度上持续存在和跨尺度推断。可实现的景观投资组合集进一步为受威胁和入侵物种的区域种群管理提供了工具。
