Big Data and AI Unlock Hidden Patterns in Nature, Transforming Conservation Efforts
In a groundbreaking development, big data and artificial intelligence (AI) are now being utilized to uncover concealed patterns in nature, revolutionizing conservation efforts. This pioneering approach goes beyond studying individual bird species and instead focuses on entire ecological communities across continents.
Researchers from the Cornell Lab of Ornithology and the Cornell Institute for Computational Sustainability have collaborated to develop and apply a computational tool that models the complete annual life cycle of each species. This includes breeding, fall migration, nonbreeding grounds, and spring migration back north. The results of this study were published on October 2 in the journal Ecology.
Lead author Courtney Davis, a researcher at the Cornell Lab, highlights the uniqueness of this method, stating, This method uniquely tells us which species occur where, when, with what other species, and under what environmental conditions. With that type of information, we can identify and prioritize landscapes of high conservation value – vital information in this era of ongoing biodiversity loss.
To generate these models, the researchers utilized data on 500 North American bird species from over 9 million checklists submitted by birders to the Cornell Lab’s eBird program. They combined this data with information on 72 environmental variables, including topography and land cover, to estimate species’ distributions, typical environments, and interactions with other species.
Advancements in AI, particularly in deep learning, and the availability of high-speed graphics processing units (GPUs) have enabled the scientists to tackle this complex computational problem. Originally developed for demanding computer games, GPUs now play a crucial role in rapidly processing vast amounts of data for AI applications.
The outcomes of this research are not limited to ornithology. The model developed by the researchers can be widely applied across various tasks as long as sufficient data is available. The team is currently working on making the outputs accessible to users without computational expertise.
One significant finding of this study is the identification of areas of high importance for North American wood warblers, a group of migratory species that are facing population decline. By pinpointing areas of highest importance throughout the year, including breeding, nonbreeding, and migratory seasons, conservation efforts can be strategically focused.
According to co-author Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science at Cornell, This model is very general and is suitable for various tasks, provided there’s enough data. The researchers are not only striving to estimate the presence and absence of bird species but also to incorporate bird calls alongside visual observations to enhance the model.
The collaboration between ecologists and experts in computer science and computational sustainability is essential for the future of biodiversity conservation. Daniel Fink, a researcher at the Cornell Lab and senior author of the study, emphasizes the significance of this cross-disciplinary approach in developing targeted plans for landscape-scale conservation, restoration, and management worldwide. The scale of the task at hand necessitates the expertise and cooperation of multiple fields.
The utilization of big data and AI in modeling hidden patterns in nature signifies a major breakthrough in conservation efforts. By combining comprehensive data sets and advanced computational techniques, researchers can gain a deeper understanding of species distributions, ecological interactions, and environmental factors contributing to declines in biodiversity. This innovative approach paves the way for more effective conservation strategies to protect and preserve our natural world.
