This article was originally written by Avanade alum Hassan Qarra.

We share our planet with millions of different species. Actually, considering the enormity of species and their habitats on this planet, we should probably say that they share it with us! Yet our impact and dominance on other species and the environments they live in, impacts them all. Ecosystems are complex communities of organisms and inanimate materials that interact to support living things. Studying and understanding them can produce intriguing facts about how they operate and how its populations live, but most importantly, teach us how we can minimise our impact on them.

Human activity is a major threat to our biodiversity and is ever increasing as our population exponentially grows and our infrastructure expands. We continue to displace more species and negatively impact the excellence of the planet’s fragile ecosystems. In order to minimise our impact, we must monitor and analyse the complex connections within and between ecosystems - to understand the impact of our human disturbances. But because up until now, collecting the relevant data has been a manual and labour-intensive process, we have not been able to obtain enough data. As a result, it’s virtually impossible to achieve a complete picture and identify important connections between elements of an ecosystem, and to make impact predictions.

The solution is leveraging data science to produce ecological behavioural insights
An impressive range of environmental and species monitoring technology exists today. The most commonly used technologies for monitoring the atmosphere include visual cameras, microphones, and radar systems. Marine technologies include sonar, hydrophones, lidar and visual cameras. All of these systems can provide a wealth of data on species and the habitats they live in. By combining this data with environmental monitoring systems, such as atmospheric weather, marine conditions etc., we can obtain a comprehensive picture of a particular area within an ecosystem. However, in practice, these technologies and systems are not regularly or comprehensively used together. Crucially, there is a lack of machine learning and data science techniques to yield high ecosystem understandings from all the generated data. We are developing our existing Microsoft Vision AI model built to monitor puffins and expanding it across multiple species.

In April, Avanade attended the Wind and Wildlife Impact Conference in the Netherlands. Our attention was drawn to the fact that, although there is adequate detection and monitoring technology, the big challenge of generating real data insights, particularly for species behaviour monitoring, remains. There is huge potential for data science to be applied to species identification and monitoring, and to connect effects of human behaviour and interventions on ecosystems. AI will play an important role in helping us to reduce our impact and emphasise best practices.

One step closer to whole ecosystem vision
An even bigger challenge is monitoring and understanding, not just a single entity within an ecosystem, but an ecosystem as a whole. By constantly monitoring an ecosystem within a defined area over time and gathering the generated data on a single platform, we are able to leverage data science models to produce insights that connect different components of an ecosystem. For example, we can cleverly correlate an abundance of a particular species of bird with an abundance of prey fish, and further correlate it to an abundance of seabed species and that to the presence of microbes. They all can be correlated with environmental conditions such as sea conditions and weather et cetera. These combined, can reveal large complex models of food-chain and environmental links on the seabed, in the sea and in the atmosphere. Relationships from gathered metadata enable the potential for complex ecosystem hypothesis to be made, that link multi-dimensional views of ecological interactions.

Alongside SSE and Microsoft, we at Avanade have developed and built a pilot for an ecological digital twin that seeks to offer a full ecosystem approach to monitoring. Imagine a full ecosystem digital system based on real-time data inputs where scientists can access lists of hypotheses automatically generated by data science models and AI, and even more, imagine being able to identify and isolate human impacts, such as the effects of electromagnetic forces of electric sea cables on fish habitats, for example. This whole ecosystem monitoring approach will instruct us how to maintain the integrity and functioning of ecosystems and crucially to avoid rapid undesirable ecological change.

A reform in ecological data transparency is looming
In spite of the fact that ecological data sharing is recognised to be invaluable, ecological networks do not have sufficient access to data nor a unified global standard for data and metadata generated. Making data more transparent is important, but vitally, standardising data so that it can easily be shared, discovered, and analysed is absolutely critical. This remains a key challenge. At the conference in April, technologists, ecologists and data scientists spoke to us about the need for improved data sharing, but also a need for tools that manage and depict data quality, to address the problem of data integration.

A vision for a global network that allows users to effortlessly identify, select, analyse and interpret ecological data has been around for a while, but there are yet to be any big steps to make it a reality. As our threat to biodiversity becomes more catastrophic, we will need faster access to data and predictive ecological insights. The current ecological crisis means this vision of ecological data sharing is essential. Ecological data must become more transparent and soon ecological data sharing may even become a regulated necessity.

Through digitalising ecological monitoring, we are able to push data streams online through standard data and metadata formats, providing users all over the world with access to data anywhere and at any time through our cloud-based technologies. Built-in analytical and visual tools will be fully accessible and constantly made more advanced. Digitalising the way we monitor our ecology is a meaningful approach to enhance data standardisation, ease of access and promoting data sharing and transparency.

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