Sophisticated analysis of large datasets – known as ‘big data’ – can enable us to meet the challenge of looking after the natural resources we depend upon for survival and economic growth, according to a new study involving the Centre for Ecology & Hydrology.
The research outlines a new approach for using environmental ‘big data’ to understand where different approaches to managing our Natural Capital (natural resources such as forests, lakes and soils that provide benefits for humans and ecosystems) are most effective, so the environment continues to provide us with the food, water, recreation and timber on which we all depend.
The study, led by the University of Southampton and also involving Forest Research and the Centre for Ecology & Hydrology (CEH), was part of a project funded by the European Research Council and has been published in the journal Nature Sustainability.
The large datasets are analysed by computer to reveal trends and patterns. To demonstrate their method, the scientists applied this to the management of forests and ponds. For their research, they used data from CEH’s UK Countryside Survey and Forest Research’s National Forest Inventory.
The research showed that the best way to manage our natural assets varies across the country, and is affected by local conditions, such as soil type, farming practices and levels of pollution.
The study demonstrated the cleanliness of Britain’s ponds is affected by how intensely the land around them is farmed, with the problem greatest in areas with sandy soils that allow polluting chemicals to flow quickly into streams and the ponds they feed. This research can show where measures to reduce the amount of chemicals used by farmers will be particularly effective for protecting our freshwater environment.
Datasets improve our understanding of the complexity of our natural environment and help determine how to manage it for future generations - Professor James Bullock
Another example highlighted in the study was the spread of rhododendron, which can inhibit the regeneration of native woods and have an adverse effect on their biodiversity by blocking sunlight to forest floors. The shrub can be managed at an early stage by removing seedlings by hand, but once established it is very costly and difficult to remove, so knowing which forests to prioritise for monitoring the arrival of the invasive plant species is important.
Rhododendron does better in acid soils, so spreads easily in these areas, but does less well in alkaline soils. However, through data analysis, the study found the proximity of a forest to other woodland in alkaline areas helps rhododendron establish and spread in these less favourable soil conditions because the nearby woods supply a source of a lot of seeds.
Professor James Bullock, an ecologist at the Centre for Ecology & Hydrology, who was involved in the research, says: “The natural resources we depend upon in our landscape are finite and managing the countryside in a way that benefits both the environment and humans, while adapting to environmental change, is a significant challenge.
“This new study shows the importance and value of the national datasets produced by CEH and Forest Research. They improve our understanding of the complexity of our natural environment and help determine how to manage it for future generations.
“Our method helps identify where and how land management measures can be taken effectively.”
Scientists involved in the study say the research shows how the UK government can achieve the ambitious goals in its 25 Year Environment Plan and make smart interventions to improve our environment.
Rebecca Spake, Chloe Bellamy, Laura J. Graham, Kevin Watts, Tom Wilson, Lisa R. Norton, Claire M Wood, Reto Schmucki, James M Bullock and Felix Eigenbrod. (2019). An analytical framework for spatially targeted management of natural capital. Nature Sustainability.
The study, involving the University of Southampton, Forest Research (the research agency of the Forestry Commission) and the Centre for Ecology & Hydrology was part of SCALEFORES, a €1.5m project funded by the European Research Council.