Data and R code from: Fire-induced loss of the world's most biodiverse forests in Latin America
Creators
- 1. Universidad Nacional de Colombia
- 2. Stony Brook University
- 3. Patagonian Ecosystems Investigation Research Center
- 4. Universidad de Aysén
- 5. University of La Frontera
- 6. Centre for Research on Ecology and Forestry Applications
Description
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.
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 provided by: National Academy of Sciences
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000209
Award Number: Subaward No 2000007526
Funding provided by: National Academy of Sciences
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000209
Award Number: Subaward No 2000010972
Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: DGE 1633299
Funding provided by: Comisión Nacional de Investigación Científica y Tecnológica
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002848
Award Number: REDI170329
Files
Code4sharing.zip
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