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Prototype Biodiversity Digital Twin: Forest Biodiversity Dynamics

Forests are crucial in supporting biodiversity and providing ecosystem services. Understanding forest biodiversity dynamics under different management strategies and climate change scenarios is essential for effective conservation and management. This paper introduces the Forest Biodiversity Dynamics Prototype Digital Twin (pDT), integrating forest and biodiversity models to predict the effects of management options on forest ecosystems.

Prototype Biodiversity Digital Twin: Disease Outbreaks

African swine fever is a transmissible virus impacting wild and domestic swine populations. In Europe, it is non-native and the recently introduced genotype affects wild boar populations with occasional outbreaks in domestic pigs. The ability to predict short-term spatial dynamics of this disease will greatly improve our ability to control and limit future spread of the virus.

Prototype Biodiversity Digital Twin: Real-time bird monitoring with citizen-science data

Bird populations respond rapidly to environmental change making them excellent ecological indicators. Climate shifts advance migration, causing mismatches in breeding and resources. Understanding these changes is crucial to monitor the state of the environment. Citizen science offers vast potential to collect biodiversity data. We outline a project that combines citizen science with AI-based bird sound classification. The mobile app records bird vocalisations that are classified by AI and stored for re-analysis.

Prototype Biodiversity Digital Twin: grassland biodiversity dynamics

European grassland management has often favoured high production through frequent mowing and heavy fertilisation over biodiversity conservation, which is typically supported by less intensive management. Besides management, climate change and extremes are increasingly affecting grassland productivity and biodiversity, requiring timely adaptation of management practices.

Evaluating FAIR Digital Object and Linked Data as distributed object systems

FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building a ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself.

Long-term monitoring of the fish community in the Minho Estuary (NW Iberian Peninsula)

The paper presents an extensive fish sampling dataset spanning a long-term period from 2010 to 2019. The data were collected in Lenta Marina, an upstream area in the Minho Estuary of the NW Iberian Peninsula, which belongs to a LTSER (Long-Term Socio-Ecological Research) platform. To capture fish, fyke nets were utilised as the sampling method and deployed at Lenta Marina. This dataset offers valuable insights into the abundance of each collected taxa recorded over time.

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