Collaborative Research: SG: Effects of altered pollination environments on plant population dynamics in a stochastic world

生物学中的一个主要挑战是了解环境中的随机波动如何影响种群及其持续生存的能力。对于植物来说，传粉者数量的下降加剧了这一点，世界各地都有记录表明这一点。人们担心，许多开花植物物种将与它们的传粉者一起衰落，因为几乎90%的开花植物依赖动物授粉来结籽、生长新植物和防止灭绝。在较长期的衰退期间，传粉者和其他环境因素(如降雨量)的短期年际波动使得识别真正的植物衰退变得更加困难。尽管植物对传粉者的依赖很强，但传粉者的减少可能不会立即导致植物的衰退，这可能会阻止我们发现植物灭绝的警告信号。例如，在传粉者减少后，植物可能会立即表现得更好，因为它会将原本用于制造种子的能量再投资于提高它们的存活率。该项目将放大数千种植物授粉的自然变异，跟踪它们的命运，并使用数学模型来理解植物如何在授粉和其他环境因素年复一年的变化中保持不变。这个项目将帮助科学家了解为什么一些植物物种比其他物种更容易受到传粉者衰退的影响。该项目将利用9-11年的植物种群和传粉者丰度的实地数据来测量和实验施加传粉者丰度的随机波动。基于个体的种群模型(积分投影模型)将用来自长期的、正在进行的田间试验的数据进行参数化，在该试验中，两种多年生植物接受不同的授粉处理：(I)增加授粉，(Ii)减少授粉，(Iii)未经操纵的对照。所有植物都标有唯一的识别号，并每年对其人口状况进行评估。种群模型的预测将展示授粉服务中的随机性对植物种群动态的影响。基于基于模型的种群预测的进一步分析将揭示当授粉者丰度波动时，哪些种群生命速率(存活、生长、繁殖)对种群动态的变化负责，此外，随机性的各个组成部分-授粉者丰度的均值、方差和自相关-如何影响植物种群动态。这个项目将阐明物种间的相互作用如何在随机世界中塑造种群动态。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

A major challenge in biology is understanding how random fluctuations in the environment affect populations and their ability to persist. For plants, this is compounded by declines in pollinators, which have been documented across the world. There is concern that many flowering plant species will decline alongside their pollinators because almost 90% of flowering plants rely on animal pollination to make seeds, grow new plants, and prevent extinction. Short-term, year-to-year fluctuations in pollinators and other environmental factors (like precipitation) during a longer-term decline make the challenge of identifying a true plant decline even harder. Despite the strong dependence of plants on pollinators, pollinator declines may not immediately cause plant declines, and this can prevent us from detecting the warning signs of plant extinction. For example, plants may appear to do better immediately after a pollinator decline by reinvesting energy that would have been used to make seeds into improving their survival. This project will amplify natural variation in pollination for thousands of plants, follow their fates, and use mathematical models to understand how plants persist despite year-to-year changes in pollination and other environmental factors. This project will help scientists to understand why some plant species are more at risk from pollinator declines than other species. The project will leverage 9–11 years of field-based data on plant demography and pollinator abundance to measure and experimentally impose stochastic fluctuations in pollinator abundance. Individual-based population models (integral projection models) will be parameterized with data from a long-term, ongoing field experiment in which two species of perennial plants receive different pollination treatments: (i) increased pollination, (ii) reduced pollination, and (iii) an unmanipulated control. All plants are tagged with a unique identification number and their demographic status assessed annually. Projections of the population models will demonstrate the consequences of stochasticity in pollination services for plant population dynamics. Further analyses based on model-based population projections will reveal which demographic vital rates (survival, growth, reproduction) are responsible for changes in population dynamics when pollinator abundances fluctuate, in addition to how various components of stochasticity – the mean, variance, and autocorrelation of pollinator abundances – affect plant population dynamics. This project will shed new light on how species interactions shape population dynamics in a stochastic world.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.