Sensitivities in associating land-system archetypes with sustainability metrics Insights from simulations

A - Papers appearing in refereed journals

Varma, V., Evans, P. M., Butikofer, L., Goodwin C. E. D., Pywell, R. F., Redhead, J. W., Storkey, J., Bullock, J. M. and Mead, A. 2024. Sensitivities in associating land-system archetypes with sustainability metrics Insights from simulations. bioRxiv. https://doi.org/10.1101/2024.08.01.606212v1

AuthorsVarma, V., Evans, P. M., Butikofer, L., Goodwin C. E. D., Pywell, R. F., Redhead, J. W., Storkey, J., Bullock, J. M. and Mead, A.
Abstract

Archetypes of land- and socio-ecological systems, generated using unsupervised classification methods, enable the assimilation of complex environmental and socio-economic information. Such simplification has considerable potential to feed into decision support systems for sustainability planning. But, the usefulness of archetypes depends on how well they relate to sustainability criteria, such as ecosystem service (ES) delivery, that are external to the input datasets employed for archetype generation. Sensitivities in such post-hoc association analyses, and the associated utility of the archetype framework in a decision support context, remain unexplored. Here we emulated post-hoc association analysis procedures using simulated socio-ecological datasets and ES response variables. Our simulations revealed a substantial influence on analysis performance from (1) the number of variables used as inputs in archetype generation, (2) the correlation structure of input datasets, (3) the type and distribution of input variables, and (4) the functional form (linear or non-linear) characterising the relationship between ES variables and their predictors. We observed near-identical performance when archetypes were generated using K-means clustering and Self-Organising Maps (SOMs) – two commonly used archetype classification methods. Further, better archetype classifier performance did not guarantee better discrimination of ES value distributions between archetypes. Our results suggest that designing a framework to generate archetypes for sustainability planning, and the selected methodological choices therein, should place greater emphasis on what the archetypes will be used for in downstream analyses, and not focus solely on archetype classifier performance. This would better ensure the identification of archetypes adaptable to a diverse array of sustainability indicators and sufficiently robust for monitoring decision outcomes over time.

KeywordsSustainability planning; Decision support; Archetypes; Ecosystem services; Clustering; Machine learning; Curse of dimensionality; Collinearity; Multi-modal distributions
Year of Publication2024
JournalbioRxiv
Digital Object Identifier (DOI)https://doi.org/10.1101/2024.08.01.606212v1
Web address (URL)https://www.biorxiv.org/content/10.1101/2024.08.01.606212v1
Open accessPublished as bronze (free) open access
FunderNatural Environment Research Council
Biotechnology and Biological Sciences Research Council
Funder project or codeNew Science to Enable the Design of Agricultural Landscapes that Deliver Multiple Functions - AgLand
Accepted author manuscript
Output statusOther
PublisherIOP Publishing Ltd
Cold Spring Harbor Lab Press, Publications Dept
ISSN1748-9326

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