Grant: Discovery Grants Program - Individual
University of British Columbia
$66,000.00
Predictive ecology in a world with dynamic data and models
EF 2022-23
Résumé
An overarching objective of many ecological research groups is to forecast ecosystems, species or processes. Indeed, for many researchers, large-scale ecological forecasts form the backbone of some of the most challenging ecological and conservation problems of today, such as building conservation strategies for wide-ranging species. One of the key challenges for large-scale studies, however, is that processes, and how their relative importance varies in space, are changing through time. Changing future conditions make it critical to understand key ecological mechanisms within a system, as empirical correlations will fail sooner without some mechanistic understanding. The long term objective is to build a new vision and reality for ecological simulation models. This involves building and using open systems based around R, building new -- and adapting our and other's existing -- ecological models that are open and interchangeable. Once these are done, we will evaluate emergent phenomena, such as stability, resilience, and feedbacks to inform land management at continental scales.The specific objectives here are 1) to build a new, large-scale, plant-functional-trait succession model, 2) test this in a wide array of systems, and 3) evaluate ecological consequences and trade-offs between vegetation management (e.g., carbon sequestration) and other management practices (e.g., caribou, fire, pest, and logging).This research will have 3 different levels of impact. First, the tools we will develop will be useful for the scientific community and its need to build new models. Second, the new models we will develop will be available for use in the new and open/interchangeable modeling and decision making community. Third, we will produce web apps that will allow the public to become involved in ecological models, allowing a transparency of decision making that has rarely been available. This will allow for a new level of outreach and connection to a much wider audience than previously envisioned for ecological models and modelers.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
British Columbia, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
RGPIN-2018-03829
Name nom
McIntire, Eliot
Department département
Forest Resources Management - Forest Resources Management
Organization id
2
Institution établissement
University of British Columbia
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2018
Award amount
66000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1503
Committee name en
Evolution and Ecology
Committee name fr
Évolution et écologie
Area of application code
201
Area of application group en
Natural resources (economic aspects)
Area of application group fr
Ressources naturelles (aspects économiques)
Area of application en
Forestry (silviculture, forest management)
Area of application fr
Foresterie (sylviculture, aménagement forestier)
Research subject code
4700
Research subject group en
Evolution and ecology
Research subject group fr
Évolution et écologie
Research subject en
Evolution and ecology
Research subject fr
Évolution et écologie
Installment
inst:5;6
Partie
2
Nb partie
6
Application title
Predictive ecology in a world with dynamic data and models
Application summary
An overarching objective of many ecological research groups is to forecast ecosystems, species or processes. Indeed, for many researchers, large-scale ecological forecasts form the backbone of some of the most challenging ecological and conservation problems of today, such as building conservation strategies for wide-ranging species. One of the key challenges for large-scale studies, however, is that processes, and how their relative importance varies in space, are changing through time. Changing future conditions make it critical to understand key ecological mechanisms within a system, as empirical correlations will fail sooner without some mechanistic understanding. The long term objective is to build a new vision and reality for ecological simulation models. This involves building and using open systems based around R, building new -- and adapting our and other's existing -- ecological models that are open and interchangeable. Once these are done, we will evaluate emergent phenomena, such as stability, resilience, and feedbacks to inform land management at continental scales.The specific objectives here are 1) to build a new, large-scale, plant-functional-trait succession model, 2) test this in a wide array of systems, and 3) evaluate ecological consequences and trade-offs between vegetation management (e.g., carbon sequestration) and other management practices (e.g., caribou, fire, pest, and logging).This research will have 3 different levels of impact. First, the tools we will develop will be useful for the scientific community and its need to build new models. Second, the new models we will develop will be available for use in the new and open/interchangeable modeling and decision making community. Third, we will produce web apps that will allow the public to become involved in ecological models, allowing a transparency of decision making that has rarely been available. This will allow for a new level of outreach and connection to a much wider audience than previously envisioned for ecological models and modelers.