Aligning evidence and action: The Research-to-Decision (RED) Framework to address modern environmental and ecological challenges
Abstract
Ecological and biodiversity crises are intensifying amid rapid advances in sensing, computation, and artificial intelligence (AI). AI offers unprecedented capabilities to understand complex ecological systems, provided outputs are interpretable, applied responsibly, and evaluated by domain experts. When addressing key challenges, science-policy frictions arise through different requirements relating to timelines, managing uncertainty, and accountability. We present the Research-to-Decision (RED) framework to better align these differences, using a transdisciplinary approach across the ecological research cycle. The framework is operationalised through embedding responsible AI, identifying where domain expertise should lead, where AI can accelerate research, and where AI-human synergy is essential for producing robust evidence for informed decision-making. Implementation pathways are identified: research & technology, communication & policy alignment, and assets & infrastructure, all underpinned by culture & people. AI synergy relies entirely upon human synergy, and through bridge scientists and institutional change, the RED framework can unlock capacity to tackle complex environmental challenges and support evidence-based policy development for biodiversity conservation.
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BibTeX
@misc{jones2026aligning,
title = {{Aligning evidence and action: The Research-to-Decision (RED) Framework to address modern environmental and ecological challenges}},
author = {Esther L. Jones and Ruth King and Stuart King and Oisin Mac Aodha and Rob James Boyd and Susan Jarvis and Matthew Silk and Jenna Lawson and David Williams and Sallie Bailey and Serge Wich and Alison Johnston and Adam Butler and Hannah Worthington and Kasim Terzic and Ben C Stevenson and Eleni Matechou and Sarah C Christofides and Eduard Campillo-Funollet and Cornelia Oedekoven and Danielle V Harris and Tiago A Marques and Altea Lorenzo-Arribas and Pete A Henrys and Carolina S Marques and Rebecca Wilks and Amanda Lenzi and Rachel McCrea},
howpublished = {EcoEvoRxiv},
year = {2026},
month = sep,
doi = {10.32942/x20q30},
url = {https://ecoevorxiv.org/repository/view/15197/},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Aligning evidence and action: The Research-to-Decision (RED) Framework to address modern environmental and ecological challenges
AU - Esther L. Jones
AU - Ruth King
AU - Stuart King
AU - Oisin Mac Aodha
AU - Rob James Boyd
AU - Susan Jarvis
AU - Matthew Silk
AU - Jenna Lawson
AU - David Williams
AU - Sallie Bailey
AU - Serge Wich
AU - Alison Johnston
AU - Adam Butler
AU - Hannah Worthington
AU - Kasim Terzic
AU - Ben C Stevenson
AU - Eleni Matechou
AU - Sarah C Christofides
AU - Eduard Campillo-Funollet
AU - Cornelia Oedekoven
AU - Danielle V Harris
AU - Tiago A Marques
AU - Altea Lorenzo-Arribas
AU - Pete A Henrys
AU - Carolina S Marques
AU - Rebecca Wilks
AU - Amanda Lenzi
AU - Rachel McCrea
PY - 2026
DA - 2026/09/28
PB - EcoEvoRxiv
DO - 10.32942/x20q30
UR - https://ecoevorxiv.org/repository/view/15197/
AB - Ecological and biodiversity crises are intensifying amid rapid advances in sensing, computation, and artificial intelligence (AI). AI offers unprecedented capabilities to understand complex ecological systems, provided outputs are interpretable, applied responsibly, and evaluated by domain experts. When addressing key challenges, science-policy frictions arise through different requirements relating to timelines, managing uncertainty, and accountability. We present the Research-to-Decision (RED) framework to better align these differences, using a transdisciplinary approach across the ecological research cycle. The framework is operationalised through embedding responsible AI, identifying where domain expertise should lead, where AI can accelerate research, and where AI-human synergy is essential for producing robust evidence for informed decision-making. Implementation pathways are identified: research & technology, communication & policy alignment, and assets & infrastructure, all underpinned by culture & people. AI synergy relies entirely upon human synergy, and through bridge scientists and institutional change, the RED framework can unlock capacity to tackle complex environmental challenges and support evidence-based policy development for biodiversity conservation.
ER -