A next-generation approach for quantifying tropical plant diversity across scales

热带森林拥有地球上大部分的植物物种。我们知道，这种生命的多样性是重要的，不仅是作为伟大的自然奇观之一，而且对于支撑全球生物地球化学循环(例如碳循环)，以及确定对气候变化的适应能力都是重要的。然而，尽管经过几个世纪的研究，我们仍然不能确切地知道热带有多少种植物，哪些地区物种最多，或者这些不同物种的丰富度是如何随着时间的变化而变化的，例如由于气候变化。围绕热带生物多样性的不确定性有两个主要原因：第一，热带森林中有数千种植物(例如，拉丁美洲热带地区的12万种植物)，其中许多看起来非常相似，这使得识别一种植物属于哪种植物也很困难(有时不可能)。目前基于形态的物种鉴定方法本质上是主观的，很难标准化，这意味着鉴定误差很高，而且大多是无法量化的。其次，热带森林幅员辽阔，而且往往很偏远，这意味着生态学家只能对整个森林面积的一小部分进行采样，而大多数热带森林仍然是科学未知的，而且很可能在未来几十年里继续如此。这两个挑战不能通过像过去几十年那样收集更多的数据来克服，而是需要在方法上进行根本性的改变。该奖学金的首要目标是建立一套统一、定量和可扩展的方法，使用新技术和现有的数据集来测量亚马逊地区的植物多样性，亚马逊是地球上最大、最多样化的热带森林。我建议使用四种独立但相辅相成的方法来实现这一目标。这一挑战的规模是巨大的；因此，我计划首先只关注最常见的树种和树科。由于这些常见物种占亚马逊地区所有树木的近20%，减少数百个物种的不确定性将对我们理解亚马逊植物生物多样性产生深远影响。首先，我将开发一种新的自动化方法来识别植物物种，方法是测量树叶样本的反射光谱，并基于这些光谱利用人工智能(AI)技术将植物分类。我将把这种方法应用于五个常见的亚马逊植物家族，它们加起来约占亚马逊地区树木个体的19%。这将为亚马逊范围内的标准化定量物种识别提供一个框架。第二，我将使用基于无人机的传感器在景观尺度(250公顷)绘制25种常见物种的地图，该传感器测量树冠的反射光谱。我将把这张无人机图像与野外验证的优势树冠树种的位置相结合，然后使用人工智能方法根据这些常见树种在整个景观中的冠层光谱来学习和绘制地图。第三，我将测试是否可以使用这些常见树种的分布作为稀有树种分布的替代。使用一种新的建模方法来明确物种间的协变，我计划利用常见树种的分布来预测稀有树种的丰富度。第四，我将测试我们可以在多大程度上利用卫星图像来扩大我们对常见树种分布的了解。卫星图像可以在整个亚马逊流域提供与植物生物多样性有关的连续信息。我建议使用现有的大量森林资源清查样地数据集来理清卫星生物多样性信号。通过专注于相同的大型普通树冠树种，我将分离出卫星传感器实际探测到的那部分植物群落。

Tropical forests hold much of Earths plant species. We know that this diversity of life is important, both in its own right as one of the great natural wonders, but also for underpinning global biogeochemical cycles (e.g. the carbon cycle), and determining resilience to climate change. Yet, despite centuries of research, we still don't know with any certainty how many of species of plants there are in the tropics, which areas have the most species, or how the abundance of these different species are changing through time, for example due to climate change.There are two main reasons for the uncertainty surrounding tropical biodiversity: First, there are thousands of plant species in tropical forests (e.g. 120,000 plant species in tropical Latin America), many of which look extremely similar, making it difficult (sometimes impossible) to identify which species an individual plant belongs too. Current approaches for species identification based on morphology are inherently subjective and difficult to standardize, meaning that identification errors are high and mostly unquantified. Second, tropical forests are vast, and often remote, meaning that ecologists are only able to sample a tiny fraction of the total forest area and most tropical forests remain unknown to science and are likely to remain so in coming decades. These two challenges cannot be overcome by collecting more data in the same way that we have for the past decades, instead a fundamental change in approach is required.The overarching goal of this fellowship is to establish a suite of unified, quantitative, and scalable approaches that use new technologies and existing datasets to measure plant diversity across Amazonia, Earth's largest and most diverse tropical forest. I propose to realize this goal using four independent yet complementary approaches. The scale of this challenge is huge; therefore, I plan to initially focus only on the most common tree species and families. Because these common species account for nearly 20% of all trees in Amazonia, reducing uncertainty in a few hundred species will have a profound impact on our understanding of Amazonian plant biodiversity.First, I will develop a new automated approach for identifying plant species by measuring reflected light spectra of leaf samples and classifying plants into species based on these spectra using artificial intelligence (AI) techniques. I will apply this approach to five common Amazonian plant families that together account for approximately 19% of individual trees in Amazonia. This will provide a framework for standardized quantitative species identifications at Amazon-wide scales.Second, I will map 25 common species at landscape scales (250ha) using a drone-based sensor that measures reflected light spectra of tree canopies. I will combine this drone imagery with field-verified locations of dominant canopy tree species, and then use AI approaches to learn and map these common tree species based on their canopy spectra across the landscape.Third, I will test if we can use the distribution of these common species as proxies for the distribution of rarer tree species. Using a new modelling approaches that explicitly for the covariation among species, I plan to predict the abundance of rare tree species using the distribution of common species.Fourth, I will test the extent to which we can scale-up our understanding of the distribution of common canopy tree species using satellite imagery. Satellite imagery can provide continuous information across the entire Amazon basin that relates to plant biodiversity. I propose to use massive existing forest inventory plot datasets to untangle the satellite biodiversity signal. By focusing on the same large common canopy tree species, I will be isolating the portion of plant communities that are actually detected by satellite sensors.