Talk outline

1. Background

DT concept in BioDT

2. Objectives

Project goals and outcomes

3. BioDT Use Cases

Practical applications

The DT concept in BioDT

Virtual representation(s) of real-world entities and processes,

synchronised at a specified frequency and fidelity

Image: digital-strategy.ec.europa.eu

The DT concept in BioDT

• Industrial DTs typically facilitate:
  • Product design
  • Operation of machinery

• In BioDT, DTs used to:
  • Mimic behaviour observed in nature
  • Meet requirements of BioDT Use Cases
  • Contribute toward EC goal of devising a full DT of the Earth

General objectives

Objective 1

Build and deploy pre-operational BioDT platform

Objective 2

Integration with RI platforms and workflows

Objective 3

Interoperability with European DT initiatives
(including DestinE) and European Data Infrastructure

Specific objectives and outcomes

1: Pre-operational BioDT platform

• Platform established on LUMI

• Case studies for model development

• Model development¹ and validation

¹ Incl. upscaling for HPC, features for interactive use

Specific objectives and outcomes

2: Integration with RIs

• APIs, user authentication and access

• Interoperability: data, software, practices

• Uptake, new user communities, training¹

¹ e.g. Bring Your Own Data hackathons

Specific objectives and outcomes

3: Interoperability with DT initiatives (incl. DestinE) and EDI

• Cross-DT synchronisation and showcases

• EOSC data integration, openly available results

• Harmonised data and data governance (EU Data Spaces)

BioDT Use Cases: overview

Use Cases split into four groups

Data from four RIs:

DISSCo, eLTER, GBIF, LifeWatch

UC Group 1: Species response to environmental change

Current status:

Existing modelling approaches insufficient

Approaches:

Hybrid modelling approaches

Combining biotic and abiotic data

HPC-compatible modelling tools

Anticipated benefits:

Improved predictions of shifts in diversity, distribution and abundance

Ability to quantify uncertainty

Tools enabling computationally demanding modelling

UC Group 1: Species response to environmental change

UC Group 2: Genetically detected biodiversity

Current status:

DNA-based methods increasingly needed (e.g. food security)

Approaches:

Models involving e.g. crop wild relatives, cryptic habitats

Incorporating DNA-based methods in DTs (e.g. for taxon IDs)

Addressing challenges specific to genetic data

Anticipated benefits:

Improved understanding of biodiversity in arable lands and soil

Applied uses (e.g. DNA-based biodiversity monitoring by SMEs)

UC Group 3: Dynamics of species of policy concern

Current status:

No reliable modelling approaches for invasive or endangered species

Challenges: e.g. data scarcity, lag effects

Approaches:

Exploiting large-scale spatial and high-resolution temporal data

New generation of predictions for invasive and endangered species

Anticipated benefits:

Improved tools to aid evidence-based ecosystem management

UC Group 4: Influence of species interactions on planetary well-being

Current status:

Multiple pressures coinciding with climate change (e.g. pandemics, pollinator loss)

Approaches:

Predicting outbreaks using e.g. pathogen distribution data

Modelling pollinator distribution and types

Maps of forage availability in agricultural landscapes

Anticipated benefits:

Information on emerging diseases and their locations

Improved knowledge of pollinator responses to environmental change

Take-home messages

BioDT will provide infrastructure to:

• Drive long-term biodiversity research
• Maintain commitments to protect biodiversity
• Safeguard societal resilience

Take-home messages

BioDT will be used to:

• Better observe spatiotemporal changes in biodiversity
• Develop an improved mechanistic understanding of these changes
• Push limits of predictive biodiversity modelling