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Harrison ME, Deere NJ, Imron MA, Nasir D, Adul, Asti HA, Aragay Soler J, Boyd NC, Cheyne SM, Collins SA, D’Arcy LJ, Erb WM, Green H, Healy W, Hendri, Holly B, Houlihan PR, Husson SJ, Iwan, Jeffers KA, Kulu IP, Kusin K, Marchant NC, Morrogh-Bernard HC, Page SE, Purwanto A, Ripoll Capilla B, de Rivera Ortega OR, Santiano, Spencer KL, Sugardjito J, Supriatna J, Thornton SA, Frank van Veen FJ, Yulintine, Struebig MJ. Impacts of fire and prospects for recovery in a tropical peat forest ecosystem. Proc Natl Acad Sci U S A 2024; 121:e2307216121. [PMID: 38621126 PMCID: PMC11047076 DOI: 10.1073/pnas.2307216121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 12/02/2023] [Indexed: 04/17/2024] Open
Abstract
Uncontrolled fires place considerable burdens on forest ecosystems, compromising our ability to meet conservation and restoration goals. A poor understanding of the impacts of fire on ecosystems and their biodiversity exacerbates this challenge, particularly in tropical regions where few studies have applied consistent analytical techniques to examine a broad range of ecological impacts over multiyear time frames. We compiled 16 y of data on ecosystem properties (17 variables) and biodiversity (21 variables) from a tropical peatland in Indonesia to assess fire impacts and infer the potential for recovery. Burned forest experienced altered structural and microclimatic conditions, resulting in a proliferation of nonforest vegetation and erosion of forest ecosystem properties and biodiversity. Compared to unburned forest, habitat structure, tree density, and canopy cover deteriorated by 58 to 98%, while declines in species diversity and abundance were most pronounced for trees, damselflies, and butterflies, particularly for forest specialist species. Tracking ecosystem property and biodiversity datasets over time revealed most to be sensitive to recurrent high-intensity fires within the wider landscape. These megafires immediately compromised water quality and tree reproductive phenology, crashing commercially valuable fish populations within 3 mo and driving a gradual decline in threatened vertebrates over 9 mo. Burned forest remained structurally compromised long after a burn event, but vegetation showed some signs of recovery over a 12-y period. Our findings demonstrate that, if left uncontrolled, fire may be a pervasive threat to the ecological functioning of tropical forests, underscoring the importance of fire prevention and long-term restoration efforts, as exemplified in Indonesia.
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Affiliation(s)
- Mark E. Harrison
- Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University of Exeter, PenrynTR10 9FE, United Kingdom
- School of Geography, Geology and the Environment, University of Leicester, LeicesterLE1 7RH, United Kingdom
| | - Nicolas J. Deere
- Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, University of Kent, CanterburyCT2 7NR, United Kingdom
| | - Muhammad Ali Imron
- Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
| | - Darmae Nasir
- Centre for the International Cooperation in Sustainable Management of Tropical Peatlands, University of Palangka Raya, Palangka Raya73112, Central Kalimantan, Indonesia
| | - Adul
- Yayasan Borneo Nature Indonesia, Palangka Raya73112, Central Kalimantan, Indonesia
| | - Hastin Ambar Asti
- Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
| | - Joana Aragay Soler
- Wildlife Conservation Research Unit, Department of Biology, University of Oxford, OxfordOX13 5QL, United Kingdom
| | - Nicholas C. Boyd
- Department of Modern Languages, University of Wales Aberystwyth, AberystwthSY23 1DE, United Kingdom
| | - Susan M. Cheyne
- School of Humanities and Social Sciences, Oxford Brookes University, OxfordOX3 0BP, United Kingdom
| | - Sarah A. Collins
- School of Biological and Marine Sciences, Faculty of Science and Engineering, University of Plymouth, PlymouthPL4 8AA, United Kingdom
| | - Laura J. D’Arcy
- Borneo Nature Foundation International, Tremough Innovation Centre, PenrynTR10 9TA, United Kingdom
| | - Wendy M. Erb
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY14850
| | - Hannah Green
- School of Biological and Marine Sciences, Faculty of Science and Engineering, University of Plymouth, PlymouthPL4 8AA, United Kingdom
| | - William Healy
- Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University of Exeter, PenrynTR10 9FE, United Kingdom
| | - Hendri
- Yayasan Borneo Nature Indonesia, Palangka Raya73112, Central Kalimantan, Indonesia
| | - Brendan Holly
- Environmental Studies, Centre College, Danville, KY40422
| | - Peter R. Houlihan
- Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA90095-1496
| | - Simon J. Husson
- Borneo Nature Foundation International, Tremough Innovation Centre, PenrynTR10 9TA, United Kingdom
| | - Iwan
- Yayasan Borneo Nature Indonesia, Palangka Raya73112, Central Kalimantan, Indonesia
| | - Karen A. Jeffers
- School of Humanities and Social Sciences, Oxford Brookes University, OxfordOX3 0BP, United Kingdom
| | - Ici P. Kulu
- Centre for the International Cooperation in Sustainable Management of Tropical Peatlands, University of Palangka Raya, Palangka Raya73112, Central Kalimantan, Indonesia
| | - Kitso Kusin
- Centre for the International Cooperation in Sustainable Management of Tropical Peatlands, University of Palangka Raya, Palangka Raya73112, Central Kalimantan, Indonesia
| | - Nicholas C. Marchant
- Wildlife Conservation Research Unit, Department of Biology, University of Oxford, OxfordOX13 5QL, United Kingdom
| | - Helen C. Morrogh-Bernard
- Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University of Exeter, PenrynTR10 9FE, United Kingdom
| | - Susan E. Page
- School of Geography, Geology and the Environment, University of Leicester, LeicesterLE1 7RH, United Kingdom
| | - Ari Purwanto
- Yayasan Borneo Nature Indonesia, Palangka Raya73112, Central Kalimantan, Indonesia
| | - Bernat Ripoll Capilla
- Borneo Nature Foundation International, Tremough Innovation Centre, PenrynTR10 9TA, United Kingdom
| | - Oscar Rodriguez de Rivera Ortega
- Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, ExeterEX4 4QF, United Kingdom
| | - Santiano
- Yayasan Borneo Nature Indonesia, Palangka Raya73112, Central Kalimantan, Indonesia
| | - Katie L. Spencer
- Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, University of Kent, CanterburyCT2 7NR, United Kingdom
| | - Jito Sugardjito
- Centre for Sustainable Energy and Resources Management, Universitas Nasional, Jakarta12520, Indonesia
- Faculty of Biology, Universitas Nasional, Jakarta12520, Indonesia
| | - Jatna Supriatna
- Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok16424, Indonesia
| | - Sara A. Thornton
- School of Geography, Geology and the Environment, University of Leicester, LeicesterLE1 7RH, United Kingdom
| | - F. J. Frank van Veen
- Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University of Exeter, PenrynTR10 9FE, United Kingdom
| | - Yulintine
- Centre for the International Cooperation in Sustainable Management of Tropical Peatlands, University of Palangka Raya, Palangka Raya73112, Central Kalimantan, Indonesia
| | - Matthew J. Struebig
- Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, University of Kent, CanterburyCT2 7NR, United Kingdom
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Lapola DM, Pinho P, Barlow J, Aragão LEOC, Berenguer E, Carmenta R, Liddy HM, Seixas H, Silva CVJ, Silva-Junior CHL, Alencar AAC, Anderson LO, Armenteras D, Brovkin V, Calders K, Chambers J, Chini L, Costa MH, Faria BL, Fearnside PM, Ferreira J, Gatti L, Gutierrez-Velez VH, Han Z, Hibbard K, Koven C, Lawrence P, Pongratz J, Portela BTT, Rounsevell M, Ruane AC, Schaldach R, da Silva SS, von Randow C, Walker WS. The drivers and impacts of Amazon forest degradation. Science 2023; 379:eabp8622. [PMID: 36701452 DOI: 10.1126/science.abp8622] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Approximately 2.5 × 106 square kilometers of the Amazon forest are currently degraded by fire, edge effects, timber extraction, and/or extreme drought, representing 38% of all remaining forests in the region. Carbon emissions from this degradation total up to 0.2 petagrams of carbon per year (Pg C year-1), which is equivalent to, if not greater than, the emissions from Amazon deforestation (0.06 to 0.21 Pg C year-1). Amazon forest degradation can reduce dry-season evapotranspiration by up to 34% and cause as much biodiversity loss as deforestation in human-modified landscapes, generating uneven socioeconomic burdens, mainly to forest dwellers. Projections indicate that degradation will remain a dominant source of carbon emissions independent of deforestation rates. Policies to tackle degradation should be integrated with efforts to curb deforestation and complemented with innovative measures addressing the disturbances that degrade the Amazon forest.
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Affiliation(s)
- David M Lapola
- Laboratório de Ciência do Sistema Terrestre - LabTerra, Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura - CEPAGRI, Universidade Estadual de Campinas, Campinas, SP, Brazil
| | - Patricia Pinho
- Instituto de Pesquisas Ambientais da Amazônia, Brasília, DF, Brazil
| | - Jos Barlow
- Lancaster Environment Centre, Lancaster University, Lancaster, UK
| | - Luiz E O C Aragão
- Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, Brazil.,Geography, University of Exeter, Exeter, UK
| | - Erika Berenguer
- Lancaster Environment Centre, Lancaster University, Lancaster, UK.,Environmental Change Institute, University of Oxford, Oxford, UK
| | | | - Hannah M Liddy
- Columbia Climate School, Columbia University, New York, NY, USA.,NASA Goddard Institute for Space Studies, New York, NY, USA
| | - Hugo Seixas
- Laboratório de Ciência do Sistema Terrestre - LabTerra, Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura - CEPAGRI, Universidade Estadual de Campinas, Campinas, SP, Brazil
| | - Camila V J Silva
- Instituto de Pesquisas Ambientais da Amazônia, Brasília, DF, Brazil.,Lancaster Environment Centre, Lancaster University, Lancaster, UK.,BeZero Carbon Ltd, London, UK
| | - Celso H L Silva-Junior
- Institute of Environment and Sustainability, University of California, Los Angeles, CA, USA.,Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.,Programa de Pós-graduação em Biodiversidade e Conservação, Universidade Federal do Maranhão - UFMA, São Luís, MA, Brazil
| | - Ane A C Alencar
- Instituto de Pesquisas Ambientais da Amazônia, Brasília, DF, Brazil
| | - Liana O Anderson
- Centro Nacional de Monitoramento e Alertas de Desastres Naturais, São José dos Campos, SP, Brazil
| | | | | | - Kim Calders
- Computational & Applied Vegetation Ecology Laboratory, Department of Environment, Ghent University, Belgium.,School of Forest Sciences, University of Eastern Finland, Joensuu, Finland
| | | | | | | | - Bruno L Faria
- Instituto Federal de Educação, Ciência e Tecnologia do Norte de Minas Gerais, Diamantina, MG, Brazil
| | | | - Joice Ferreira
- Empresa Brasileira de Pesquisa Agropecuária, Belém, PA, Brazil
| | - Luciana Gatti
- Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, Brazil
| | | | | | - Kathleen Hibbard
- National Aeronautics and Space Administration Headquarters, Washington, DC, USA
| | - Charles Koven
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Peter Lawrence
- National Center for Atmospheric Research, Boulder, CO, USA
| | - Julia Pongratz
- Max Planck Institute for Meteorology, Hamburg, Germany.,Ludwig-Maximilians University of Munich, Munich, Germany
| | | | - Mark Rounsevell
- Karlsruhe Institute of Technology, Karlsruhe, Germany.,University of Edinburgh, Edinburgh, UK
| | - Alex C Ruane
- NASA Goddard Institute for Space Studies, New York, NY, USA
| | | | | | - Celso von Randow
- Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, Brazil
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Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning. REMOTE SENSING 2022. [DOI: 10.3390/rs14112510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.
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Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data. REMOTE SENSING 2022. [DOI: 10.3390/rs14071545] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Fire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal changes in forest aboveground biomass (AGB), measured in permanent plots, and in traditional spectral indices derived from Landsat-8 images. We tested if the spectral indices can improve Random Forest (RF) models of post-fire AGB losses based on pre-fire AGB, proxied by AGB data from immediately after a fire. The delta normalized burned ratio, non-photosynthetic vegetation, and green vegetation (ΔNBR, ΔNPV, and ΔGV, respectively), relative to pre-fire data, were good proxies of canopy damage through tree mortality, even though small and medium trees were the most affected tree size. Among all tested predictors, pre-fire AGB had the highest RF model importance to predicting AGB within one year after fire. However, spectral indices significantly improved AGB loss estimates by 24% and model accuracy by 16% within two years after a fire, with ΔGV as the most important predictor, followed by ΔNBR and ΔNPV. Up to two years after a fire, this study indicates the potential of structural and spectral-based spatial data for integrating complex post-fire ecological processes and improving carbon emission estimates by forest fires in the Amazon.
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The Effects of Environmental Changes on Plant Species and Forest Dependent Communities in the Amazon Region. FORESTS 2022. [DOI: 10.3390/f13030466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
We review the consequences of environmental changes caused by human activities on forest products and forest-dependent communities in the Amazon region—the vast Amazonas River basin and the Guiana Shield in South America. We used the 2018 and 2021 Intergovernmental Panel on Climate Change reports and recent scientific studies to present evidence and hypotheses for changes in the ecosystem productivity and geographical distribution of plants species. We have identified species associated with highly employed forest products exhibiting reducing populations, mainly linked with deforestation and selective logging. Changes in species composition along with a decline of valuable species have been observed in the eastern, central, and southern regions of the Brazilian Amazon, suggesting accelerated biodiversity loss. Over 1 billion native trees and palms are being lost every two years, causing economic losses estimated between US$1–17 billion. A decrease in native plant species can be abrupt and both temporary or persistent for over 20 years, leading to reduced economic opportunities for forest-dependent communities. Science and technology investments are considered promising in implementing agroforestry systems recovering deforested and degraded lands, which could engage companies that use forest products due to supply chain advantages.
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Even organic pesticides spur change in the wildlife next door. Nature 2021. [PMID: 34795430 DOI: 10.1038/d41586-021-03445-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Singh M, Zhu X. Analysis of how the spatial and temporal patterns of fire and their bioclimatic and anthropogenic drivers vary across the Amazon rainforest in El Niño and non-El Niño years. PeerJ 2021; 9:e12029. [PMID: 34707922 PMCID: PMC8502451 DOI: 10.7717/peerj.12029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 07/30/2021] [Indexed: 11/20/2022] Open
Abstract
In the past two decades, Amazon rainforest countries (Brazil, Bolivia, Colombia, Ecuador, Guyana, Peru and Venezuela) have experienced a substantial increase in fire frequency due to the changes in the patterns of different anthropogenic and climatic drivers. This study examines how both fire dynamics and bioclimatic factors varied based on the season (wet season and dry season) El Niño years across the different countries and ecosystems within the Amazon rainforest. Data from publicly available databases on forest fires (Global Fire Atlas) and bioclimatic, topographic and anthropogenic variables were employed in the analysis. Linear mixed-effect models discovered that year type (El Niño vs. non-El Niño), seasonality (dry vs. wet), land cover and forest strata (in terms of canopy cover and intactness) and their interactions varied across the Amazonian countries (and the different ecosystems) under consideration. A machine learning model, Multivariate Adaptive Regression Spline (MARS), was utilized to determine the relative importance of climatic, topographic, forest structure and human modification variables on fire dynamics across wet and dry seasons, both in El Niño and non-El Niño years. The findings of this study make clear that declining precipitation and increased temperatures have strong impact on fire dynamics (size, duration, expansion and speed) for El Niño years. El Niño years also saw greater fire sizes and speeds as compared to non-El Niño years. Dense and relatively undisturbed forests were found to have the lowest fire activity and increased human impact on a landscape was associated with exacerbated fire dynamics, especially in the El Niño years. Additionally, the presence of grass-dominated ecosystems such as grasslands also acted as a driver of fire in both El Niño and non-El Niño years. Hence, from a conservation perspective, increased interventions during the El Niño periods should be considered.
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