A framework for identifying carbon hotspots and forest management drivers

Spatial analyses of ecosystem system services that are directly relevant to both forest management decision making and conservation in the subtropics are rare. Also, frameworks that identify and map carbon stocks and corresponding forest management drivers using available regional, national, and int...

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Detalles Bibliográficos
Autores Principales: Escobedo, Francisco Javier, Timilsina, Nilesh, Wendell, P, Cropper, Jr, Brandeis, Thomas J, Delphin, Sonia, Lambert, Samuel
Formato: Artículo (Article)
Lenguaje:Inglés (English)
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://repository.urosario.edu.co/handle/10336/27453
https://doi.org/10.1016/j.jenvman.2012.10.020
Descripción
Sumario:Spatial analyses of ecosystem system services that are directly relevant to both forest management decision making and conservation in the subtropics are rare. Also, frameworks that identify and map carbon stocks and corresponding forest management drivers using available regional, national, and international-level forest inventory datasets could provide insights into key forest structural characteristics and management practices that are optimal for carbon storage. To address this need we used publicly available USDA Forest Service Forest Inventory and Analysis data and spatial analyses to develop a framework for mapping “carbon hotspots” (i.e. areas of significantly high tree and understory aboveground carbon stocks) across a range of forest types using the state of Florida, USA as an example. We also analyzed influential forest management variables (e.g. forest types, fire, hurricanes, tenure, management activities) using generalized linear mixed modeling to identify drivers associated with these hotspots. Most of the hotspots were located in the northern third of the state some in peri-urban areas, and there were no identifiable hotspots in South Florida. Forest silvicultural treatments (e.g. site preparation, thinning, logging, etc) were not significant predictors of hotspots.