Structural diversity: a digital revolution

Abstract

One of the fundamental rules in biology and ecology is that structure determines function. At a microscale, the transmissibility of COVID-19 is related to the unique structure of the novel coronavirus's trimeric spike protein. At a macroscale, the success of early human settlement along the west coast of North America during the Holocene is likely associated with the rich resources provided by the diverse structures of kelp forests. Until recently, research on ecosystem structure and diversity has lagged, due primarily to a lack of tools to measure these components effectively and accurately. For instance, scientists studying deciduous forests have relied on stem size and leaf area as proxies of forest structure to predict ecosystem functions. However, these traditional, proxy-based metrics are laborious to collect, logistically challenging to assess at large scales, and fail to capture other critical aspects of structural diversity such as canopy depth and complexity, both of which provide better predictions of ecosystem functions and processes as compared to their conventional counterparts. Fortunately, innovations in digital technology and data science are converging to yield a wide range of new research capabilities. As demonstrated by the papers in this Special Issue, ecologists are starting to embrace these promising advances, unlocking opportunities to understand structural diversity. We now have a robust suite of sensors that can be deployed on different platforms and used to map and measure three-dimensional (3D) structures in both terrestrial and aquatic systems, across a wide range of scales and resolutions. For example, modern single-photon lidar can provide wall-to-wall, high-resolution, 3D point cloud data in less than one hour for an area of over 10,000 ha, allowing individual tree-level measurement. Spanning large geographic regions, these unprecedented 3D data will enable investigations of fundamental ecological questions (eg macroscale patterns, scaling) that traditional plot-based approaches cannot. As with other innovations, however, these structural data also bring unencountered challenges. To take full advantage of ecology's digital advances, we argue that the following challenges must be addressed. The first is the necessity to ensure truly transdisciplinary collaboration. Although recognized for decades, the demand for such collaboration has never been greater in 3D structural diversity enabled ecology, especially at its current early stages of development. Most of the skillsets essential to harnessing and realizing the potential of these new technologies currently lie in disciplines beyond ecology and the environmental sciences. We need expertise from aviation technology for stable and safe data acquisition, engineering solutions for sensor integration, geomatics for data registration, computer science for data integration and segmentation, graphic design for visualization, information science for managing data and ensuring fair access, and social science for understanding societal implications and unintended consequences, to name a few. The quickest way for ecologists and environmental scientists to incorporate these research skills into their own work is through collaboration. The second challenge is embracing novel computational platforms (eg super computers, cloud computing) and approaches (eg machine learning and artificial intelligence). When covering large spatial extents, these 3D data often range from gigabytes to terabytes to petabytes in size. Therefore, advanced computing power and algorithms are required to handle them. Third, to better utilize these data, investigators will have to shift from traditional hypothesis-driven approaches to data-driven approaches or to a hybrid of the two. As these 3D data can generate hundreds of variables measuring different aspects of ecosystem structure, making sense of the data, especially when combined with other climatic and geophysical datasets, can be daunting or nearly impossible. Such massively multivariate datasets can quickly overwhelm traditional process-based modeling approaches. When deployed by knowledgeable analysts, machine learning approaches – whether data driven or domain knowledge guided – may reveal insights into ecosystem patterns and processes. Lastly, there is a pressing need to educate our next generation to embrace these digital advances. New data-oriented skills such as acquisition, visualization, analysis, and management of large datasets must become essential parts of ecological training. Consideration of structural diversity is foundational to our understanding of ecological systems. Consequently, advances in 3D data acquisition and analytics will likely reshape our understanding of ecology. Adopting these emerging digital technologies will enable the next generation of ecologists to gracefully operate a fleet of sensors to measure ecosystems and swim freely in the resulting ocean of data.