Noisy Data
Challenges in Capturing the Noisy Data of Endangered Seed Diversity
The rare endemic flora of Hawai’i faces over 50% extinction risks from compounding anthropogenic threats and invasive species crowding out vulnerable genera. Seed banks offer genetic savings accounts, but cataloging efforts race against habitat degradation. The Lyon Arboretum battles extinction fog by digitizing endangered seeds from 63 families into an accessible visual library to aid conservation. Machine learning promises accelerated identification of scarce specimens. However, with 41% of native plants endangered, manual imaging struggles to scale on pace with extinction trajectories. Computational techniques offer tools to push past data bottlenecks by extracting signals from the noise, particularly for rare or inaccessible specimens. The correct phenotypes must proliferate faster than the last remaining in situ specimens vanish.
Island ecosystems and archipelagos, such as Hawai’i, are home to extraordinary plant diversity but are also under significant threat, endangering more than half of their native species. Seed banks serve as crucial genetic reservoirs to safeguard against these threats to biodiversity. However, various challenges on the ground introduce uncertainties into these conservation efforts.
The extinction of specific pollinators, advancing urban development, the displacement of species by invasive ones, and the unpredictability of climate change, along with difficulties in accessing certain areas, greatly hinder the ability of researchers to precisely document the seeds of endangered native plants. Unpredictable factors conceal at-risk germplasm, making it difficult for even the most determined conservationists to catalog them comprehensively. The dense thicket of environmental challenges effectively conceals rare plant seeds from view.
Nevertheless, accurate identification is critical. The Lyon Arboretum has undertaken a thorough imaging project covering 63 families of plants at risk, creating lasting visual records to penetrate the cloud of biodiversity challenges. Photographing each rare seed eliminates a degree of uncertainty in identification. Once these images are digitized and made available online, even the most obscure species can be recognized and appreciated, rising above the noise created by invasive species.
However, with 41% of Hawai’i’s native flora classified as endangered, the question remains: Can the production of these images keep pace sufficiently to enable effective conservation actions? This initiative seeks to leverage machine learning to stay ahead of the curve of extinction, offering a beacon of hope for preserving biodiversity.