10.5061/DRYAD.9CNP5HQH6
Armenteras, Dolors
0000-0003-0922-7298
Universidad Nacional de Colombia
Davalos, Liliana María
Stony Brook University
Barreto, Joan Sebastian
Universidad Nacional de Colombia
Hernandez-Moreno, Angela
Patagonian Ecosystems Investigation Research Center
Zamorano-Elgueta, Carlos
Universidad de Aysén
Gonzalez-Delgado, Tania
Universidad Nacional de Colombia
Meza, Maria Constanza
Universidad Nacional de Colombia
Miranda, Alejandro
University of La Frontera
Retana, Javier
Centre for Research on Ecology and Forestry Applications
Data and R code from: Fire-induced loss of the world’s most biodiverse
forests in Latin America
Dryad
dataset
2021
National Academy of Sciences
https://ror.org/038mfx688
Subaward No 2000007526
National Academy of Sciences
https://ror.org/038mfx688
Subaward No 2000010972
National Science Foundation
https://ror.org/021nxhr62
DGE 1633299
Agencia Nacional de Investigación y Desarrollo
https://ror.org/02ap3w078
REDI170329
2021-06-30T00:00:00Z
2021-06-30T00:00:00Z
en
12258 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Fire plays a dominant role in deforestation, particularly in the tropics,
but the relative extent of transformations and influence of fire frequency
on eventual forest loss remain unclear. Here we analyze the frequency of
fire and its influence on post-fire forest trajectories between 2001-2018.
We account for ~1.1% of Latin American forests burnt in 2002-2003
(8,465,850 ha). Although 40.1% of forests (3,393,250 ha) burned only once,
by 2018~48% of the evergreen forests converted to other, primarily
grass-dominated uses. While greater fire frequency yielded more
transformation, our results reveal the staggering impact of even a single
fire. Increasing fire frequency imposes greater risks of irreversible
forest loss, transforming forests into ecosystems increasingly vulnerable
to disturbance and degradation. Reversing this trend is indispensable to
both mitigate and adapt to climate change globally. As climate change
transforms fire regimes across the region, key actions are needed to
conserve Latin American forests.
Data and R code for alluvial plots and Bayesian analyses for Fire-induced
loss of the world’s most biodiverse forests in Latin America Address data
queries to darmenterasp@unal.edu.co and Bayesian R code related queries to
liliana.davalos@stonybrook.edu 1) Place all files in a single folder, 2)
alluvial_2step.R makes two-step alluvial plots that require further
editing to look as in the paper, 3) alluvial_countries.R makes single step
alluvial plots for each country, 4) poisson_overdisperse.R runs MCMCglmm
models on the overdispersed Poisson-distributed data and saves them to an
RData file needed for plotting coefficients, 5) poisson_plot.R plots the
coefficients for the best fit model, requires RData output from step 4.