DIGITAL TWINS FOR ECOLOGY AND NATURE CONSERVATION: AN INTERVIEW WITH ANNA DAVISON
Featured image: Groundstation.Space, photo by Kacia Rutkoŭskaja
The Nature FIRST project makes use of digital twins to create model-driven, continuous ecosystem monitoring beyond simple species counts. These digital twins serve as a means for learning, improving monitoring models, and translating environmental observations into actionable information for site managers and policymakers. But what are some common misconceptions around the concept of a digital twin? What are some challenges they face, and how can they assist decision-makers in safeguarding our natural ecosystems? And what is their role in the Nature FIRST project? Join us for an interview with Anna Davison, a PhD Candidate at Wageningen University & Research, as she answers these questions and more.
What is a digital twin?
As explained by Anna, a digital twin is essentially a dynamic model of a real-world entity or system that is continuously updated in real-time, to mirror its physical counterpart. Therefore, the key difference between a digital twin and a traditional static model lies in its real-time synchronisation with the real-world system it represents, she notes. In essence, a digital twin integrates expert knowledge, historical data, and real-time information to create a constantly usable resource. It mobilises all available knowledge and data about a specific subject into a tool that can guide decision-makers and researchers.
Anna highlights that one of the intriguing aspects of digital twins is their versatility in terms of spatial and temporal scales. They can represent anything from a square metre of land to an entire country, depending on the specific application and objectives. This flexibility allows researchers and decision-makers to tailor digital twins to their unique needs. In this sense, the scale at which a digital twin operates depends on the goals and requirements of the project. For instance, it can focus on the entire climate system, a single country's climate, or even a specific ecological niche within a habitat. The choice of scale determines the level of detail and precision the digital twin can offer, Anna explains.
There are common misconceptions surrounding digital twins. Some view them as overly complex systems, while others perceive them as nothing more than dashboards or models of everything. Anna points out that in reality, digital twins can be as simple or complex as needed, depending on their intended purpose. They are adaptable tools designed to address specific ecological challenges. They are also designed, as highlighted by Anna, to be user-friendly and low-tech. They do not require high-end computing resources to operate, as they are intended to run smoothly on common devices like smartphones and computers. However, this transition necessitates one essential element: a stable internet connection. The seamless operation of digital twins, along with data flow, is contingent on reliable internet connectivity.
Anna finds it important to note that while digital twins may seem like a novel concept, they share common principles with traditional modelling techniques. Just like traditional models, digital twins require a clear understanding of the subject matter, precise spatial and temporal boundaries, and a validation process to ensure accuracy.
Meanwhile, the real innovation lies in their real-time capabilities and ongoing relevance. Unlike static models that serve a specific purpose and become obsolete, digital twins remain active tools, constantly updated and providing insights as new data becomes available.
Featured image: Wageningen University & Research
Challenges for digital twins
As pointed out by Anna, the adoption of digital twins in ecology faces various challenges, some of which are, on the one hand, related to the importance of transdisciplinary research. Ecologists, engineers, and researchers from various fields often work in isolation, leading to a lack of communication of ideas and techniques. Anna highlights that bridging these gaps and fostering collaboration among experts from diverse backgrounds is crucial for the successful implementation of digital twins in ecology.
On the other hand, in the conservation field, there is a noticeable shift towards adopting digital tools and technologies. These tools offer advanced capabilities, such as tracking endangered species and monitoring ecosystem health. However, a pervasive issue persists—the lack of sufficient funding. Inadequate financial resources create substantial gaps in the data required to effectively operate digital twins. Without adequate funding, obtaining real-time and up-to-date sensor data remains a significant challenge.
In addition, for digital twins to flourish in the conservation domain, access to real-time data is imperative. The capacity to capture and analyse data as events unfold in the natural world is vital for the functionality of these digital replicas, and the absence of real-time data is akin to attempting navigation without a reliable compass—a challenge that must be addressed to facilitate effective conservation.
However, as noted by Anna, perhaps the most significant human-related challenge is streamlining the integration of data into digital twins. A user-friendly approach is essential, with data entry processes aligning with the ease of traditional methods, such as manual note-taking. The transition to digital should be frictionless, offering clear advantages to users.
Importantly, the conservation landscape is linguistically diverse, posing another challenge. Digital twins must seamlessly accommodate multiple languages and regions. This demands the development of advanced algorithms capable of interpreting and translating data seamlessly, ensuring comprehensibility and consistency.
Moreover, the ethical and legal aspects of data ownership and sharing are pivotal. Questions related to data ownership, particularly when dealing with sensitive information like poaching risks and species locations, demand meticulous attention. Balancing data accessibility with privacy is a complex task, necessitating careful navigation by conservationists.
The future and evolving nature of digital twins
Digital twins are still evolving in the field of ecology, and there is no standardised approach or set of rules for creating them. Researchers are pioneering the development of digital twins for various ecological applications, but challenges such as data sharing, model accuracy, and adaptability persist.
Thus, looking ahead, the future of digital twins in ecology is promising yet complex. The success of digital twins hinges on their ability to accurately represent ecological systems, adapt to changing conditions, and facilitate evidence-based decision-making.
The ideal scenario involves creating digital twins that can be used by non-experts, such as policymakers and conservationists, to make informed decisions. However, this vision requires the establishment of standardised methods, extensive testing, and ongoing collaboration among experts in the field.
As digital twins gradually find their place in the world of conservation, a steep learning curve will be encountered. Each project will serve as a building block, revealing successful approaches and areas for improvement. Success is not solely defined by the creation of functional tools; it also hinges on understanding their limitations, effectiveness in the field, and adaptability to various scenarios. Through iterative development, robust and flexible tools can emerge.
The role of digital twins in the Nature FIRST Project
In the context of the Nature FIRST project, digital twins play a key role. Anna explains: their primary objective is to harness the wealth of data available at each field site, transforming it into a dynamic and accessible tool. This tool serves a multitude of functions, including enabling field researchers to visualise their data in real-time, facilitating data sharing with policymakers, and ensuring that the information remains current and relevant to the conservation challenges at hand.
At its core,as noted by Anna, the purpose of these digital twins is to empower conservationists with the ability to make informed, evidence-based decisions. These decisions can range from addressing specific issues like sturgeon location tracking, human-wildlife conflict management, assessing poaching risks, to monitoring biodiversity indicator levels. The Nature FIRST project takes a multifaceted approach to measuring success in the implementation of digital twins, through three levels.
The foundational level revolves around creating a practical digital twin that accurately predicts conservation aspects relevant to field sites, aiding in addressing challenges effectively. Level two entails minimising uncertainty and achieving a high degree of accuracy and reliability, ensuring trustworthiness in generated insights. Finally, level three refers to adoption and utility: the ultimate success lies in active adoption by conservationists and policymakers, with the tool proving invaluable in guiding real-world conservation decisions. In other words, success for the project means making the tools work well, making them highly accurate, and making sure people use them for real conservation work.
The Nature FIRST project believes that their digital twins can bring big changes to conservation. These tools can give conservationists the power to make better decisions using data, and as they continue to develop, they have the potential to bring big improvements to how we protect biodiversity on our planet.
The Crane Radar is the first version of a Digital Twin developed within Nature FIRST. Featured image: Sensing Clues
Digital twins for nature conservation
Digital twins represent a revolution in conservation, marking a shift from manual record-keeping to data-driven decision-making. While there are notable challenges to overcome, such as securing funding, ensuring real-time data access, and integrating diverse datasets, these obstacles are not insurmountable. They require dedication and innovative solutions. The human element in this journey is not merely about overcoming hurdles; it's about sparking imagination, nurturing curiosity, and expanding the realm of what is achievable.
The world of conservation stands on the brink of a digital transformation. Digital twins have the potential to revolutionise ecological research and transform how conservation decisions are made. These digital replicas, powered by real-time data, expert knowledge, and historical information, offer a dynamic approach to comprehending and managing intricate ecological systems. Despite lingering challenges and misconceptions, the continuous evolution of digital twins in ecology holds the promise of a more informed and sustainable future for environmental science and conservation.
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