Generalized Boosted Models and analysis scripts for fire occurrence, Alaska, 1950-2009

This nested dataset provide the results from the generalized boosted modelling (GBMs) (also referred to as boosted regression tree (BRT) modelling) from Young et al. (2017). These models are designed to predict the spatial probability of fire occurrence at 4-km 2 spatial resolution and 30-yr tempora...

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Bibliographic Details
Main Author: Adam M. Young
Format: Dataset
Language:unknown
Published: Arctic Data Center 2015
Subjects:
EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > FIRE ECOLOGY > FIRE MODELS
EARTH SCIENCE > AGRICULTURE > FOREST SCIENCE > FOREST FIRE SCIENCE
EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > FIRE ECOLOGY > FIRE OCCURRENCE
EARTH SCIENCE > BIOSPHERE > TERRESTRIAL ECOSYSTEMS > ALPINE/TUNDRA
OTHER
GRID
1 KILOMETER TO 10 KILOMETERS
DECADAL
biota
climatologyMeteorologyAtmosphere
geoscientificInformation
Tundra
Alaska
Online Access:https://doi.org/10.18739/A26688J13
Description
Summary:This nested dataset provide the results from the generalized boosted modelling (GBMs) (also referred to as boosted regression tree (BRT) modelling) from Young et al. (2017). These models are designed to predict the spatial probability of fire occurrence at 4-km 2 spatial resolution and 30-yr temporal resolution in Alaskan boreal forest and tundra ecosystems. Within this nested dataset are five zip files. Together, the contents of these zip files provide the data and tools necessary to run the GBM models and recreate the results from Young et al. (2017). Further details on the specific contents in each zip file are provided in the README file for this nested dataset. Citation: Young, A.M., P. E. Higuera, P. A. Duffy, and F. S. Hu. 2017. Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change. Ecography 40:606-617. doi: 10.1111/ecog.02205.

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