³² Environmental DNA (eDNA) can reveal hidden diversity within communities³³ and is becoming increasingly accurate.³⁴ Imaging technologies can identify species, predict physiological processes³⁵ and analyse ecological functions even in complex ecosystems.³⁶ Acoustic monitoring can reveal changes in ecosystems,³⁷ including underwater.³⁸ Chemical sensors can detect volatile organic compounds,³⁹ many of which are used as signals. There is growing scope for remote monitoring,^40,41 including from space.⁴² On the scale of individual organisms, tracking devices are increasingly small and smart. AI is being embedded in all these tools.

Alongside these technological advances, there is a second growing source of information and knowledge: Indigenous and local knowledge (ILK). While Indigenous groups have often been shut out of research and conservation,⁴³ in more recent decades there have been calls to generate more comprehensive and equitably produced data,⁴⁴ and resulting action.⁴⁵ This more inclusive approach has improved the assessment of endangered-species status⁴⁶ and enabled more effective conservation action.⁴⁷ While combining ILK and normal science can be challenging, conservationists need to build on these early successes and further enrich their work with ILK.⁴⁸

This blizzard of new datasets poses a challenge: how do we integrate so many diverse types of data? Can we build them into a global picture — and is that even a useful thing to do? AI could help us process and understand these enormous datasets, but only if they are sufficiently systematic to be learnable.⁴⁹ However, such projects inevitably raise issues of privacy and data sovereignty.