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)
Describing, modelling and managing changes in biodiversity is hampered because conventional monitoring is limited in spatial, temporal and taxonomic coverage and is often poorly standardised. Automated sensors now promise to change this: DNA extracted from water, soil or air (environmental DNA, eDNA), passively recording microphones, and cameras whose images are identified by machine learning each produce, together with the pipelines that process their output, inventories of whole communities without a human identifying the species. It is tempting to treat such data as more of the same kind, to be fed into the established analyses of species ranges and niches. The paper challenges this assumption.
“Novel community data” is the paper’s term for inventories of whole communities produced by automated sensors and pipelines, and the paper argues that they differ from conventional inventories in kind, not only in quantity. Novel community data typically follow fixed protocols with controls, so they depend less on individual observers. Their errors are real but usually more consistent, and therefore easier to correct statistically. They can reach finer spatial, temporal and taxonomic resolution, come with rich metadata, and can record interactions between species directly, for example from gut contents. Exploiting these properties, the paper argues, needs dedicated analysis. Joint species distribution models describe all species of a community together and estimate associations among them beyond their shared responses to the environment. Time-series methods use the order of events to separate cause from correlation. Network analysis and explicit models of observation error complete the set.
For the paper, data and methods therefore need to be developed together. Combined, they could address long-standing questions about how communities assemble and how biotic interactions shape where species occur, and they could support biodiversity governance. An existing example is the UK’s licensing scheme for the great crested newt, where eDNA surveys for the newt underpin a risk-zone map and a market for compensation habitat.