One of the grand challenges in ecology is to understand and predict how ecosystems are impacted by changes in environmental conditions and external pressures. The urgency of this challenge is high given the unprecedented rate of global change in climate, land use, and urbanisation. To do so, we need to transform ecology into a predictive science. Scenarios can be used to explore how complex ecosystems could behave under different external pressures. Developing scenarios requires combining knowledge and data. This is analogous to global climate models, which also integrate fundamental knowledge and empirical data on climate processes to forecast consequences of different global emission scenarios. Key for developing high-quality scenarios is the availability of long-term environmental and ecological datasets. 

A Digital Twin is a digital replica of a living or non-living physical entity. Digital Twins allow advanced data-driven modelling and simulation, using Big Data tools to generate novel insights that cannot be obtained with traditional observation models. Building Digital Twins of ecosystems has only recently became possible as Big Data, artificial intelligence (AI) applications and analytics, advanced computing infrastructure, and the FAIR principles have been developed and made available for ecology, ecosystem restoration, and biodiversity science. A Digital Twin of an ecosystem also provides tools to integrate data on abiotic factors (e.g., nutrient deposition, temperature, droughts), biotic factors (e.g., long-term occurrence data on animals and plants, life history data), and human activities (e.g., tourism, agriculture, fishery). Digital Twins consist of diagnostic (data-driven) and dynamic (process-based) models and couples ‘‘Big Data’’ on ecosystems with well-developed process-based models on the relationships between species and their environment. 

This collection hosts the outcomes of the LTER-LIFE project. LTER-LIFE develops an infrastructure that includes a Virtual Research Environment (VRE) and services (e.g., catalogues and repositories that contain FAIR data, models, and software tools) that together can instantiate custom Virtual Laboratories to build Digital Twins of specific ecosystems.