Fire propensity in Amazon savannas and rainforest and effects under future climate change
Mariana Martins Medeiros de Santana
A * , Rodrigo Nogueira de Vasconcelos B and Eduardo Mariano-Neto C
+ Author Affiliations
- Author Affiliations
A Engenharia Florestal, Universidade do Estado do Amapá (UEAP), Av. Pres. Vargas, 650 - Central, 68900-070, Macapá (Amapá), Brazil.
B Modelagem em Ciências da Terra e do Ambiente, Universidade Estadual de Feira de Santana (UEFS), Av. Transnordestina, 44036-900, Feira de Santana (Bahia), Brazil.
C Instituto de Biologia, Universidade Federal da Bahia (UFBA), Rua Barão de Jeremoabo, s/n, Ondina, 40170-115, Salvador (Bahia), Brazil.
* Correspondence to: mariana.medeiros@ueap.edu.br, eng.marimedeiros@gmail.com
International Journal of Wildland Fire 32(2) 149-163 https://doi.org/10.1071/WF21174
Submitted: 2 November 2021 Accepted: 21 October 2022 Published: 16 November 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Background: Fire dynamics in the Amazon, while not fully understood, are central to designing fire management strategies and providing a baseline for projecting the effects of climate change.
Aims: The study investigates the recent fire probabilities in the northeastern Amazon and project future ‘fire niches’ under global warming scenarios, allowing the evaluation of drivers and areas of greatest susceptibility.
Methods: Using the maximum entropy method, we combined a complex set of predictors with fire occurrences detected during 2000–2020. We estimated changes in fire patterns in the near (2020–2040) and distant (2080–2100) future, under two contrasting scenarios of shared socioeconomic pathways.
Key results: Based on current conditions, the spatial fire pattern is affected by farming activities and fire is more common in savannas than in forests. Over long time scales, changes toward a warmer and drier climate, independent of land cover change, are expected to create conditions more conducive to burning.
Conclusion and implications: Our study helps in understanding the multiple ecological and human interactions that result in different fire regimes in the Amazon. Future efforts can improve outcomes through more complex models that couple predictions of land use and land cover changes, shifts in vegetation resulting from climate change and fires, and fuel dynamics.
Keywords: Amazon, climate change, disturbance, fire niche, fire risk, fire susceptibility, MaxEnt, modelling, remote sensing, wildfires.
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