Skip to main content
Dryad logo

Data and R code from: Fire-induced loss of the world’s most biodiverse forests in Latin America

Citation

Armenteras, Dolors et al. (2021), Data and R code from: Fire-induced loss of the world’s most biodiverse forests in Latin America, Dryad, Dataset, https://doi.org/10.5061/dryad.9cnp5hqh6

Abstract

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.

Usage Notes

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.

 

 

Funding

National Academy of Sciences, Award: Subaward No 2000007526

National Academy of Sciences, Award: Subaward No 2000010972

National Science Foundation, Award: DGE 1633299

Comisión Nacional de Investigación Científica y Tecnológica, Award: REDI170329