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

跨尺度量化热带植物多样性的下一代方法

基本信息

批准号：
NE/V014323/1
负责人：
Frederick Draper
金额：
$ 79.99万
依托单位：
University of Liverpool
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FV014323%2F1
关键词：
next generation approach quantifying tropical

项目摘要

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.

热带森林拥有地球上的许多植物物种。我们知道，生命的多样性是重要的，不仅是因为它本身是伟大的自然奇迹之一，而且是因为它支撑着全球生物化学循环（例如碳循环），并决定了对气候变化的适应能力。然而，尽管经过几个世纪的研究，我们仍然不确定热带地区有多少种植物，哪些地区拥有最多的物种，或者这些不同物种的丰度如何随着时间的推移而变化，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：首先，热带森林中有数千种植物物种（例如拉丁美洲热带地区有120，000种植物物种），其中许多看起来极其相似，因此很难（有时不可能）识别一株植物属于哪个物种。目前基于形态学的物种识别方法本质上是主观的，难以标准化，这意味着识别错误很高，而且大多数是不可量化的。其次，热带森林面积广阔，而且往往偏远，这意味着生态学家只能对森林总面积的一小部分进行采样，大多数热带森林仍然不为科学所知，而且在未来几十年内可能仍然如此。这两个挑战不能通过收集更多的数据来克服，我们已经在过去的几十年里，相反，在方法上需要一个根本性的变化。这个奖学金的总体目标是建立一套统一的，定量的，可扩展的方法，使用新技术和现有的数据集来测量亚马逊，地球上最大的和最多样化的热带森林的植物多样性。我建议通过四种独立但互补的方法来实现这一目标。这个挑战的规模是巨大的;因此，我计划最初只关注最常见的树种和家庭。由于这些常见的物种占亚马逊所有树木的近20%，减少几百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的影响。首先，我将开发一种新的自动化方法，通过测量叶片样品的反射光谱，并根据这些光谱使用人工智能（AI）技术将植物分类为物种，从而识别植物物种。我将把这种方法应用到五个常见的亚马逊植物家族中，它们加起来约占亚马逊河流域树木总数的19%。这将提供一个框架，标准化的定量物种识别在亚马逊范围内scales.Second，我将映射25个常见的物种在景观尺度（250公顷）使用无人机为基础的传感器，测量反射光谱的树冠。我将联合收割机将这些无人机图像与经实地验证的主要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树冠光谱来学习和绘制这些常见树种的地图。第三，我将测试我们是否可以使用这些常见树种的分布作为稀有树种分布的代理。使用一种新的建模方法，明确的物种之间的协变，我计划预测丰富的稀有树种使用常见物种的分布。第四，我将测试在何种程度上，我们可以扩大我们的了解，常见的树冠树种的分布，使用卫星图像。卫星图像可以提供整个亚马逊流域与植物生物多样性有关的连续信息。我建议使用大量现有的森林资源清查样地数据来解开卫星生物多样性信号。通过关注相同的大型常见树冠树种，我将隔离卫星传感器实际检测到的植物群落部分。