Monitoring and reporting outcomes of Nature-based Solutions projects

The Agile Initiative Knowledge Hub includes a tool for selecting metrics for monitoring biodiversity and soil health outcomes in NbS.

Research completed by NbSI and Agile Initiative to inform this resource highlighted factors that are considered in every project when developing a monitoring plan:

• What to measure – selecting informative metrics.
• How to measure – identifying effective, ideally standardised, methodologies for data collection.
• Interpreting results – assessing results to understand project outcomes.

Developing monitoring approaches  

Nature-based Solutions projects usually have multiple desired outcomes, alongside tackling societal challenges, they should support biodiversity and local communities. For example, in a project aiming to mitigate climate change, alongside demonstrated carbon sequestration, clear benefits for biodiversity and local people should be very well evidenced.

Biodiversity and soil health are central to the ecological integrity of NbS and can present challenges to measure, given their multifaceted nature. Following Noss’ hierarchy of biodiversity [1], which categorises biodiversity into compositional, structural, and functional elements, and grouping soil health indicators into biological, chemical and physical categories [2], we prioritised metrics based on their ease of monitoring and informativeness. This process also highlighted the multitude of metrics available for monitoring, often with limited associated information on their effectiveness.

Pairing metrics with methodologies for data collection adds an additional challenge. Ideally, data would be collected in a standardised way across all NbS projects, maximising the possibility of cross-project comparisons and synthesis of multiple projects. However, even in the UK, which has fairly well-developed ecological monitoring programmes, standardised methodologies are not readily available for many of the recommended biodiversity and soil health metrics.

Data is only as useful as its interpretation. The purpose of data collection and how it will be used to assess outcomes should be the first step in monitoring design. Understanding how trends will be inferred, either by assessing change over time or by comparison to a control site, is a crucial part of the design process.

Lack of data can limit policy impacts – an example of carbon in rewilding 

The recent Grantham Institute report “Exploring the carbon sequestration potential of rewilding in the UK” highlighted that incorporating rewilding into UK net zero commitments is currently limited by available evidence and the ability to effectively monitor outcomes3. Given the expected contribution of rewilding to carbon sequestration and emissions reduction, filling these gaps is an important policy goal.

Rewilding presents a twofold challenge for projecting benefits from projects. Firstly, the nature-led approach means that projects will often have unpredictable trajectories. Secondly, carbon sequestration estimates are poorly characterised for the habitats resulting from rewilding e.g. mosaics of grassland and scrub.

A unified and effective approach for monitoring outcomes of nature restoration projects is an important goal, particularly when project outputs are used for offsetting. This will ensure that any benefits from projects are accurately balanced against the negative impacts they are offsetting.  The report encourages government to “consider the merits of nominating or creating an organisation to capture and manage carbon and greenhouse gas flux data (alongside wider socioeconomic and ecological data) from nature restoration projects” [3].

A recent study assessed the suitability of an existing standardised tree biomass estimation method, which combines tree measurements with allometric equations to predict carbon storage, in the context of regenerating trees and scrub at the Knepp rewilding project in southern England [4]. The study found that, at Knepp, belowground biomass was around four times that predicted by the models, and on average biomass belowground was slightly greater than aboveground. This highlights that we often lack the underlying data needed to accurately estimate the carbon benefits of rewilding projects.

Measurement in the context of offsetting 

In a world where accurate measurement of project outcomes is crucial – from biodiversity to carbon storage – we need frameworks, tools, and protocols to ensure accurate, robust, and repeatable monitoring. In particular, when outputs are balanced against an offsetting commitment, as is increasingly the case, accurate assessment of project outcomes becomes even more critical.

References: 

1. Noss, R. F. Indicators for Monitoring Biodiversity: A Hierarchical Approach. Conserv. Biol. 4, 355–364 (1990).
2. Jian, J., Du, X. & Stewart, R. D. A database for global soil health assessment. Sci. Data 7, 3–10 (2020). 
3. Mercer, L. & Gregg, R. Exploring the carbon sequestration potential of rewilding in the UK: policy and data needs to support net zero. www.cccep.ac.uk (2023).  
4. Burrell, N. C., Jeffers, E. S., Macias-Fauria, M. & Willis, K. J. The inadequacy of current carbon storage assessment methods for rewilding: A Knepp Estate case study. Ecol. Solut. Evid. 5, 1–12 (2024). 

Related Projects: 

Biodiversity and ecosystem function responses to woodland creation

Scaling-up nature-based solutions in the UK