WHY PREDICTIVE DATA IS VITAL FOR NATURE CONSERVATION

Predictive data is prevalent across industries and disciplines, and it plays an important role in nature conservation and the prevention of biodiversity loss. By analysing and predicting patterns and trends in wildlife populations and habitats, we can make more informed decisions about how to protect and preserve natural resources. But how does it actually work? In this blog post, we explore the role of predictive data in nature conservation, and why it’s a key element for Nature FIRST. 

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Predictive Data Analytics

Data analytics is the practice of examining data to answer questions, identify trends, and extract insights. There are different types of data analytics, and predictive data analytics answers the question of what might happen in the future. 

Predictive data analytics is used more often than it may seem: for weather forecasts, to avoid traffic, for the stock market, or medical advancements. These examples use a combination of real-time and historical data to forecast future scenarios and, if necessary, prevent them through strategic decisions. This can be done for both short-term and long-term developments. 

When it comes to climate change, scientists make use of predictive data analytics to build climate models, with the purpose of understanding how our planet is changing. The Paris Agreement, for example, was reached based on predictive data, as scientists aim to limit global warming to 2 degrees Celsius and in this way, mitigate the “worst effects” of climate change. 

Similarly, predictive data analytics can also be used for nature conservation. Since nature conservation aims at protecting and preserving the Earth’s biodiversity and the natural systems that support it, efforts to conserve nature are also efforts to halt biodiversity loss. But why is it so important to do this?

Biodiversity Loss 

Biodiversity loss entails the decline of variety in species and ecosystems and has far-reaching consequences for both humanity and the environment. Biodiversity facilitates processes such as pollination, pest control, and water retention or purification (as it regulates the water cycle). In this sense, biodiversity is a key component of healthy ecosystems. This means that the loss of species and habitats can lead to imbalances in the food chain and ecosystem functions—for example, the loss of a predator can result in overpopulation of its prey, and so on. 

Biodiversity also helps preserve the resilience of ecosystems and their ability to adapt to change: ecosystems with high biodiversity can more easily withstand and recover from environmental stressors. It is therefore essential to protect and conserve biodiversity to ensure a healthy and sustainable future for our planet. 

In order to effectively address biodiversity loss, nature conservation efforts must be informed by a deep understanding of what is causing it, along with which strategies are effective for conservation and restoration purposes. This can be done through predictive data analytics. 

Predictive Data Analytics for Nature Conservation

On the one hand, predictive data allows us to identify potential threats or problems before they become significant. For example, if a predictive model shows that a particular species of bird is losing its habitat at an alarming rate, then we can take action to protect the species before it becomes endangered. 

On the other hand, predictive data can help us prioritise certain efforts: as we identify areas and species that are in need of most protection, we can direct our efforts accordingly in order to have the greatest impact. For example, suppose we want to protect an endangered turtle species and a model predicts that the turtle is likely to do well in a particular region that has been heavily impacted by habitat loss. Then we can focus our conservation efforts in that area to restore the habitat, remove invasive species, and reduce human disturbance. We can also target our conservation education efforts to the local communities in the region. All of this has the purpose of focusing our resources so they will have the greatest impact on preserving biodiversity. 

In addition, predictive data can help track the success of conservation efforts over time. By monitoring changes in wildlife populations and habitats, we can determine if our efforts are having the desired effect. So if we develop a tool with the purpose of protecting habitats or species, we can correctly evaluate whether it works or not and adjust the strategy if needed. 

For example, if we implement a conservation programme aimed at protecting a particular species of butterfly that is facing habitat loss, we can gather data on the population size and distribution of the butterfly before and after the implementation of the programme, as well as data on environmental factors that could be important. Using this data, we can build a predictive model that takes into account the relationship between the butterfly’s population size and the environmental factors that affect its survival. We can also use the model to track the actual changes in the butterfly’s population size over time, and compare them to our predictions. If the model’s predictions and the actual results vary, we can investigate the reasons for the discrepancy and make changes to the programme to improve effectiveness. 

It is important to note that in the context of nature conservation, predictive data analytics highly depends on the modelling of ecosystems. It is through the use of different types of models, such as digital twins (computer models of an ecosystem in a specific area), that we can simulate the behaviour of ecosystems and the interactions between their components (such as species, habitats, and the climate). In this way, it is possible to analyse both historical and real-time data and, in turn, make predictions of the future state of ecosystems, in order to inform conservation efforts. 

This is exactly what we do at Nature FIRST, starting with European field sites but aiming far beyond. For instance, in the Danube Delta River, we know that sturgeons are highly endangered due to poaching and pollution, so we are developing digital twins to model their migration patterns along the river and protect them more effectively. To learn more about digital twins, you can read last week’s interview with Koen de Koning, and to learn more about other tools that we use, visit our home page and sign up for our newsletter.