EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models
Lead Research Organisation:
Bangor University
Department Name: Sch of Natural Sciences
Abstract
If the United Nations sustainable development goals (SDGs; https://sustainabledevelopment.un.org/) are to be achieved, it is vital to understand the interactions between people and nature. A significant aspect of these interactions can be classed as 'nature's contributions to people' (termed ecosystem services; ES). However, the empirical ES data needed to quantify these relationships are sparse in all parts of the World. Using recent advances in data availability from remote sensing, models are increasingly able to provide credible information where empirical data are lacking. Specifically, ES models produce maps of estimated ES (typically based on land cover and other driving variables) and so can provide the understanding of the spatial distribution and heterogeneity ES required to aid planning and optimisation of land use decisions. However, most ES modelling applications rely on a single model for each ES and few applications explicitly validate ES models against independent datasets. As a consequence, the uncertainties associated with each application of ES models (and the datasets that underpin) them remain largely unknown. This is a particular issue as the results of local-scale validation are likely not to be transferable to new locations or to the regional and national scales at which ES model outputs are most widely used.
EnsemblES seeks to address these issues by: 1) investigating ES model input sensitivity, varying initial conditions at the start of model simulations; 2) combining the outputs of multiple ES models (from multiple initial conditions) into 'ensembles' of models using a variety of techniques including when data on individual model performance is vs is not available; and 3) validating these model ensembles against independent data, highlighting a) the accuracy of ES ensembles, and b) whether coefficients of variation of the ensemble is a good predictor of ensemble uncertainty.
EnsemblES seeks to address these issues by: 1) investigating ES model input sensitivity, varying initial conditions at the start of model simulations; 2) combining the outputs of multiple ES models (from multiple initial conditions) into 'ensembles' of models using a variety of techniques including when data on individual model performance is vs is not available; and 3) validating these model ensembles against independent data, highlighting a) the accuracy of ES ensembles, and b) whether coefficients of variation of the ensemble is a good predictor of ensemble uncertainty.
Planned Impact
The societal impact of EnsemblES will be to contribute to the attainment of the Sustainable Development Goals (SDGs; https://sustainabledevelopment.un.org/), by enhancing the tools available to decision-makers to manage ecosystem services (ES). Specifically, the findings of EnsemblES will enable decision makers to: 1) best use existing ES models to inform national and regional land use/cover change policies supporting ES management and promoting equality and justice amongst the beneficiaries of these ES; and 2) set priorities determining where scarce resources should be invested to improve effective management of ES. The primary beneficiaries will be regional and national decision-makers within the UK, and the secondary beneficiaries include academics and researchers working in cognate fields, as well as similar decision-makers with international remits.
Organisations
Publications
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Hooftman D
(2022)
Reducing uncertainty in ecosystem service modelling through weighted ensembles
in Ecosystem Services
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Willcock S
(2019)
A Continental-Scale Validation of Ecosystem Service Models
in Ecosystems
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Kim H
(2023)
Towards a better future for biodiversity and people: Modelling Nature Futures
in Global Environmental Change
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Cooper GS
(2020)
Regime shifts occur disproportionately faster in larger ecosystems.
in Nature communications
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Willcock S
(2023)
Earlier collapse of Anthropocene ecosystems driven by multiple faster and noisier drivers
in Nature Sustainability
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Hutchings P
(2022)
Understanding rural-urban transitions in the Global South through peri-urban turbulence
in Nature Sustainability
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Willcock S
(2021)
Nature provides valuable sanitation services
in One Earth
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Tenbrink T
(2023)
Place attachment and perception of climate change as a threat in rural and urban areas.
in PloS one
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Willcock S
(2023)
Model ensembles of ecosystem services fill global certainty and capacity gaps.
in Science advances
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Willcock S
(2020)
Ensembles of ecosystem service models can improve accuracy and indicate uncertainty
in Science of The Total Environment
Title | Ensembles of ecosystem service models |
Description | We produced a 90sec animation describing the benefits of using ensembles of ecosystem service models. This was released as the video abstract of a paper, but also shared with key decision makers |
Type Of Art | Film/Video/Animation |
Year Produced | 2021 |
Impact | None to date |
URL | https://www.sciencedirect.com/science/article/pii/S221204162100156X |
Description | The project found that ensembles of ecosystem service models can indeed improve accuracy and indicate uncertainty. Researchers used validation data across a data-deficient area to test the accuracy of ensembles of models against the accuracy of individual models. This comparison found evidence that had at minimum a 5-17% higher accuracy than a randomly selected individual model and, in general, ensembles weighted for among model consensus provided better predictions than unweighted ensembles. Although sometimes individual models can give good predictions, without validation against data or other models there is the potential for serious negative consequences to occur if estimates deviate significantly from the truth. For this reason, ensembles of models should be widely adopted so that sustainable development decisions can be made to protect the ecosystem services we all rely on. Our analysis suggests various ensemble methods should be applied depending on data quality, for example if validation data are available. |
Exploitation Route | Future scientists and decision-makers using ecosystem service models should use multiple different models together to look at the average outcomes. We suggest which methods of combining models are best. |
Sectors | Digital/Communication/Information Technologies (including Software),Environment |
URL | https://landscapedecisions.org/ensembles/ |
Description | Our findings are currently being summarised in a ~2min animation. We disseminated this animation via virtual workshops with key stakeholders (as detailed in our proposal). In addition, via Landscape Decisions, we held real-time panel sessions of 'national' level key stakeholders (i.e., Defra, FR, HS etc, EN, National Trust, NFU, CLA etc). |
First Year Of Impact | 2022 |
Sector | Digital/Communication/Information Technologies (including Software),Environment |
Impact Types | Societal |
Description | Email correspondance with Dr Phil Raymond | Evidence Team Leader (International Biodiversity & Environment Negotiations) | UK IPBES National Focal Point |
Geographic Reach | National |
Policy Influence Type | Citation in other policy documents |
Description | Between environmental concerns and compliance: How does media messaging affect motivation and choice between disposable versus reusable facemasks? |
Amount | £343,974 (GBP) |
Funding ID | AH/W003813/1 |
Organisation | Arts & Humanities Research Council (AHRC) |
Sector | Public |
Country | United Kingdom |
Start | 06/2021 |
End | 05/2022 |
Description | Scaling up Off-Grid Sanitation |
Amount | £1,749,830 (GBP) |
Funding ID | ES/T007877/1 |
Organisation | Economic and Social Research Council |
Sector | Public |
Country | United Kingdom |
Start | 07/2020 |
End | 09/2024 |
Description | The impact of Covid-19 restrictions on recreation and use of green space in Wales |
Amount | £10,877 (GBP) |
Funding ID | ES/V004077/1 |
Organisation | Economic and Social Research Council |
Sector | Public |
Country | United Kingdom |
Start | 06/2020 |
End | 11/2021 |
Title | Ensemble outputs among contemporary ecosystem service models for water supply and aboveground carbon storage in the UK following 10 different methods |
Description | This data set contains UK-wide maps of ten different among-model ensemble approaches for two services: above ground Carbon stock and water supply. The data for Carbon comes as fourteen TIF maps for above ground carbon storage at a 1-km2 resolution with associated world files: ten approaches, with a double option for two of those, together with maps of variation among models and among ensembles. For water, the data comes as one shapefile with polygons per watershed, each polygon containing these fourteen estimates. For all maps, 600dpi jpg depictions are added to the supporting information. Directory location independent layer files are included to aid scaling and providing the colour palettes. Ensemble output maps were calculated with different approaches following the supporting documentation and associated publication. Uncertainty estimates for these services are included as variation among contributing model outputs and among the employed ensemble approaches. The work was completed under the 'EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models' project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme. |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | None to date |
URL | https://catalogue.ceh.ac.uk/id/a9ae773d-b742-4d42-ae42-2b594bae5d38 |
Title | Ensemble outputs from Ecosystem Service models for water supply, aboveground carbon storage and use of water, grazing, charcoal and firewood by beneficiaries in sub-Saharan Africa |
Description | This dataset contains the gridded estimates per 1 km2 for mean and median ensemble outputs from 4-6 individual ecosystem service models for Sub-Saharan Africa, for above ground Carbon stock, firewood use, charcoal use and grazing use. Water use and supply are identically supplied as polygons. Individual model outputs are taken from previously published research. Making ensembles results in a smoothing effect whereby the individual model uncertainties are cancelled out and a signal of interest is more likely to emerge. Included ecosystem service models were: InVEST, Co$ting Nature, WaterWorld, Monetary value benefits transfer, LPJ-GUESS and Scholes models. Ensemble outputs have been normalised, therefore these ensembles project relative levels of service across the full area and can be used, for example, for optimisation or assignment of most important or sensitive areas. The work was completed under the 'EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models' project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://catalogue.ceh.ac.uk/id/11689000-f791-4fdb-8e12-08a7d87ad75f |
Title | Global ensembles of Ecosystem Service map outputs modelled at 1km resolution for water supply, recreation, carbon storage, fuelwood and forage production |
Description | This data set contains Global maps of five ecosystem services using 6 different among-model ensemble approaches: the provisioning services of water supply, biomass for fuelwood and forage production, the regulating service Carbon Storage for CO2 retention and the cultural non-material service Recreation. For water, the data comes as one shapefile with polygons per watershed, each polygon containing seven ensemble estimates. The other services - recreation, carbon storage, biomass for fuelwood and forage production - come as seven tiff- maps at a 1-km2 resolution with associated world files for each tiff-map contains 43,200 x 18,600 pixels for one ensemble approach, with LZW compressed file sizes between 400MB and 950MB. For all maps, 600dpi jpg depictions are added to the supporting information with uniform colour scaling set for the median ensemble per service. Ensemble output maps were calculated with different approaches following the supporting documentation and associated publication. Uncertainty estimates for these services are included as variation among contributing model outputs and among the employed ensemble approaches. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | None yet (associated publication is still in-review) |
URL | https://catalogue.ceh.ac.uk/documents/bd940dad-9bf4-40d9-891b-161f3dfe8e86 |