Novel community data in ecology — properties and prospects
Abstract
New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce “novel community data” as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning.
What the paper shows and why it matters (AI-generated)
eDNA, passive acoustic recorders and optical sensors now generate automated community-level observations at a scale traditional surveys never could — but analysing them with statistical tools designed for old-style presence/absence data risks wasting most of what's new about them. This review names the resulting field “novel community data” and maps out what its statistical properties actually are and what tools are needed to exploit them. Two years on, it's already been cited widely across exactly the communities it addresses — eDNA metabarcoding, bioacoustics, joint species distribution modelling — suggesting the framing has been useful rather than just descriptive.