Grant: Alliance Grants
McGill University
$562,500.00
AI and Biodiversity Change (ABC)
EF 2023-24
Résumé
Climate change poses a major threat to biodiversity globally, and the ability to detect and respond to change within populations, species, and ecosystems is critical to mitigating the ongoing biodiversity crisis. As policymakers, land managers, and communities struggle to protect species and preserve or restore ecosystems, there has been an urgent need for data on how species abundances and distributions are changing and tools to assess the efficacy of policies and interventions. However, these data and tools remain very limited, due to the effort and expertise needed for effective data-gathering and tool development, the fast pace of ecological change, and imbalances in resources across geographies and taxonomic groups.Artificial Intelligence (AI) systems are well positioned to help fill the information gap, via massively scalable data streams such as satellite imagery, visual and audio sensors, environmental DNA, and participatory science data platforms. AI could enable a new paradigm of biodiversity change monitoring, making it possible to detect changes across taxonomic scales (from populations to ecosystems), spatial scales (from site to continental), and temporal scales (days to decades) that would be undetected with existing data streams and more traditional survey approaches.We propose a Global Climate Center on AI and Biodiversity Change (ABC) bringing together ecologists and AI researchers, together with stakeholders such as nonprofits, citizen scientists, and policymakers, to co-design and deploy AI-enabled solutions for biodiversity. Specifically, we will study cross-cutting research questions in biodiversity change, design innovative AI algorithms to match these challenges, publish open-source tools for community use, and co-design educational programs and resources to train AI and ecology stakeholders.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
585136-2023
Name nom
Rolnick, David DS
Department département
Computer Science, School of
Organization id
61
Institution établissement
McGill University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2023
Award amount
562500
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
400
Area of application group en
Environment
Area of application group fr
Environnement
Area of application en
Environment
Area of application fr
Environnement
Research subject code
4700
Research subject group en
Evolution and ecology
Research subject group fr
Evolution and ecology
Research subject en
Évolution et écologie
Research subject fr
Évolution et écologie
Application title
AI and Biodiversity Change (ABC)
Keyword
artificial intelligence; biodiversity change; climate change; ecology; machine learning
Application summary
Climate change poses a major threat to biodiversity globally, and the ability to detect and respond to change within populations, species, and ecosystems is critical to mitigating the ongoing biodiversity crisis. As policymakers, land managers, and communities struggle to protect species and preserve or restore ecosystems, there has been an urgent need for data on how species abundances and distributions are changing and tools to assess the efficacy of policies and interventions. However, these data and tools remain very limited, due to the effort and expertise needed for effective data-gathering and tool development, the fast pace of ecological change, and imbalances in resources across geographies and taxonomic groups.Artificial Intelligence (AI) systems are well positioned to help fill the information gap, via massively scalable data streams such as satellite imagery, visual and audio sensors, environmental DNA, and participatory science data platforms. AI could enable a new paradigm of biodiversity change monitoring, making it possible to detect changes across taxonomic scales (from populations to ecosystems), spatial scales (from site to continental), and temporal scales (days to decades) that would be undetected with existing data streams and more traditional survey approaches.We propose a Global Climate Center on AI and Biodiversity Change (ABC) bringing together ecologists and AI researchers, together with stakeholders such as nonprofits, citizen scientists, and policymakers, to co-design and deploy AI-enabled solutions for biodiversity. Specifically, we will study cross-cutting research questions in biodiversity change, design innovative AI algorithms to match these challenges, publish open-source tools for community use, and co-design educational programs and resources to train AI and ecology stakeholders.