We live in a time of unprecedented environmental change, brought about by the impacts of human activity. Important global drivers for this environmental change include:

  • Changes in how we manage and use our land.
  • Pollution.
  • Invasive species.
  • Emergent diseases.
  • Climate. 

All these drivers interact from local to global scales to alter the composition of species and the ecosystem services which regulate the environment. As a result, global change could have a major impact on our wellbeing.

The rapid rate of change and complex feedbacks in socio-ecological systems makes the trajectory of future environmental change a major area of uncertainty. Therefore, an urgent challenge for scientists and decision-makers alike is to improve our ability to predict the consequences of environmental changes on the species populations, environmental processes, and ecological systems that underpin future ecosystem service provision.

Environmental decisions need models

The impacts of human activities on ecological systems are increasingly regulated by policy, and growing amounts of environmental data is being collected to monitor and support these decisions. However, understanding the long-term effects of management and regulatory decisions on ecosystems cannot be achieved with data alone. For instance, human impacts on ecosystems are often obscured by interactions between various management and ecological factors, acting at different spatial and temporal scales. Projecting historical data into the future is therefore unlikely to account for novel and rapidly-changing socio-economic and environmental conditions.

Models can help to address these issues, by representing simplified versions of real ecosystems and by including the key mechanisms that link environmental and anthropogenic drivers to ecosystem dynamics. Models therefore represent indispensable tools for supporting environmental decisions, by allowing decision-makers to explore - or ‘optioneer’ - the ecological impacts of their decisions, before implementation.

Cranfield’s Centre for Environmental and Agricultural Informatics (CEAI) research team address challenges across the spectrum of environmental monitoring and modelling, with expertise across remote sensing, air quality, environmental information systems, and developing environmental life cycle assessment and modelling tools to support environmental management and policy decisions. Dr Alice Johnston joined CEAI in April 2020 and brings to the group her expertise in mechanistic models at local scales and statistical approaches at global scales, with a focus on soil ecosystems.

Modelling soil population responses to environmental change at local scales

Soils harbour vastly diverse biological communities, which play key roles in the multiple soil functions (e.g. carbon and nutrient cycling) that are essential for ecosystem service provision. Soil biota also display divergent responses to environmental changes, and collective shifts in the composition of soil communities can have dramatic consequences for terrestrial ecosystems.

At local scales, mechanistic models of soil populations allow species population dynamics to emerge from detailed individual physiological and behavioural processes, and the interactions between individuals and their changing environment1. Mechanistic models can therefore provide detailed predictions about the consequences of different in-field management practices (e.g., plant protection products, tillage) on soil biota population dynamics well into the future.

Model outcomes can be used to support ecological risk assessments for key bioindicators of soil health, such as earthworm populations in various management schemes and environmental change scenarios2. However, at landscape to global scales complex feedbacks between soil communities and their environment necessitate the use of statistical approaches. Many of the mechanisms that link environmental drivers, soil communities and ecosystem functions are also not well known at large spatial scales. Stochastic approaches – the collection of random variables - help to test a range of possible drivers, and ensemble approaches – combining the use of several models/algorithms - allow central tendencies to emerge alongside a characterisation of uncertainty.

Linking soil communities and ecosystem functions at a global scale

Soils store most of the Earth’s terrestrial carbon (C) and so they are critical in determining the trajectory of future climate changes. If soils release more carbon dioxide (CO2) into the air with increasing temperatures than they store, then a positive soil-climate feedback would further accelerate climate change. This rate of CO2 release from soils – known as soil respiration – is the conversion of organic C from plant inputs, to CO2 by all the organisms that live in the soil. We would therefore expect soil respiration rates to shift according to the soil biota inhabiting an ecosystem and their sensitivity to environmental drivers.

Dr Johnston’s research has demonstrated the influence of soil communities on soil respiration at different temperatures in this way, by linking the metabolic rates of soil biota, and composition of soil communities to the temperature sensitivity (how quickly respiration increases with temperature) of soil respiration across global biomes3. More recently, Dr Johnston utilised high-resolution ecosystem respiration data (comprising over 23.5 million measurements) to show the presence of temperature thresholds to carbon cycling processes at a global scale4. In contrast to the Earth System Models used to predict climate changes, which assume that ecosystem respiration doubles with an increase in temperature of 10 °C, this finding indicates a much greater and lower temperature sensitivity of ecosystem respiration in cold and warm climates, respectively, with important implications for the global net land carbon sink.

The local mechanisms driving global patterns in ecological energy and nutrient flows still need to be disentangled by understanding the sensitivity of different ecosystem components (e.g. aboveground, belowground, plant, and biotic) to several environmental and anthropogenic drivers. Such advances in our knowledge have important consequences for predicting future biosphere feedbacks with climate changes. But advancing our understanding of complex socio-ecological systems demands a new digital environmental science.

Towards a digital environment

Regardless of the approach, models are increasingly used to support environmental decisions. Advances in computing power and the availability of vast bodies of environmental data has enabled scientists to build increasingly detailed models to better understand and predict ecosystem dynamics. ‘Forecasting’, ‘hindcasting’ and ‘nowcasting’ of viable scenarios aid environmental scientific understanding and the decision space for environmental regulators and policy makers. The advantages of models, however, have not yet been fully realised across the breadth of policy makers, businesses, communities and individuals who would benefit from modelling-based decision support.

To address this need, Professor Ron Corstanje and Professor Stephen Hallett in CEAI have been appointed Champions of the Natural Environment Research Council’s strategic priorities fund ‘Constructing a Digital Environment’. The programme aims to draw together expertise in digital monitoring and modelling across a breadth of disciplines to identify how digital technology can support assessments of the natural environment and forecasts of environmental change in greater detail than has previously been possible.

Bridging the gap between science and decision-making

Models create a nexus between science and policy, but not all models are equal and are not always appropriate to address the problem at hand. Models used early on in the Covid-19 pandemic provide an example of this, where contradictory outputs from numerous models caused confusion and underscored the need for clear communication of models and their context. A recent paper co-authored by Dr Johnston proposes three screening questions that can help critically evaluate models with respect to their purpose, organisation, and evidence, and enable more secure use of models for key decisions by policy makers5.

Models will be increasingly used to support future environmental decisions, such as those set out in the UK Government’s 25-year environment plan, the green recovery or as a result of the UK’s withdrawal from the EU. Further advances in technology will bring about new kinds of computational tools, but with it the need for a new consensus on how to combine multiple tools to represent complex socio-ecological systems.


1. Johnston, A. S. A. et al. 2019. Predicting population responses to environmental change from individual-level mechanisms: towards a standardized mechanistic approach. Proceedings of the Royal Society B: Biological Sciences 286, 2019 1916.

2. Johnston, A. S. A., Sibly, R. M. & Thorbek, P. 2018. Forecasting tillage and soil warming effects on earthworm populations. Journal of Applied Ecology 55, 1498–1509.

3. Johnston, A. S. A. & Sibly, R. M. 2018. The influence of soil communities on the temperature sensitivity of soil respiration. Nature Ecology & Evolution 2, 1597–1602.

4. Johnston, A. S. A. et al. Temperature thresholds of ecosystem respiration at a global scale. Nature Ecology & Evolution (in revision)

5. Grimm, V., Johnston, A. S. A., Thulke, H.-H., Forbes, V. E. & Thorbek, P. 2020. Three questions to ask before using model outputs for decision support. Nature Communications 11, 4959.