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Fire enhances forest degradation within forest edge zones in Africa

Nature Geoscience volume 14, pages 479–483 (2021)Cite this article

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Abstract

African forests suffer from severe fragmentation that further causes forest degradation near forest edges. The impact of fires used for slash-and-burn on forest edge effects remains unclear. Here, using high-resolution satellite-based forest-cover and biomass datasets, we find that edge effects extend a median distance and an interquartile range of \(0.11_{ - 0.04}^{ + 0.06}\,{\mathrm{km}}\) and \(0.15_{ - 0.05}^{ + 0.09}\,{\mathrm{km}}\) into moist and dry forests, and biomass within the forest edge zones has a carbon deficit of 4.1 PgC. Fires occurred in 52% of the forest edges and increased the carbon deficit by \(5.5_{ - 2.9}^{ + 4.3}\,{\mathrm{MgC}}\,{\mathrm{ha}^{{-1}}}\), compared with non-fire edges, through both the direct impact of fires intruding into forests and the indirect impact of changes in the local atmospheric circulations increasing canopy dryness. If small-scale slash-and-burn practices continue, increased fragmentation during 2010–2100 will result in a carbon loss from edge effects of 0.54–4.6 PgC. Fragmentation-caused forest degradation could be avoided by implementing dedicated forest protection policies supported by satellite monitoring of forest edges.

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Fig. 1: Scales and magnitudes of forest edge effects in Africa.
Fig. 2: Difference of forest edge effects at fire and non-fire edges for moist and dry forests and Δscale change with Δβ and fire distance.
Fig. 3: Future carbon loss due to forest edge effects and deforestation in different scenarios under RCP 2.6 and RCP 8.5.

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Data availability

The 30 m forest-cover map (v1.7) from Hansen et al.21 can be downloaded from https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html. The 25 m full-resolution GlobBiomass map for Africa from Santoro et al.23 can be downloaded from https://zenodo.org/record/4725667. The global and official 100 m GlobBiomass map22 can be downloaded from http://globbiomass.org/products/global-mapping or https://doi.pangaea.de/10.1594/PANGAEA.894711. The fire data from FireCCI24 can be downloaded from https://doi.org/10.5285/58f00d8814064b79a0c49662ad3af537. Source data are provided with this paper.

Code availability

All additional computer codes are available from the corresponding author on request.

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Acknowledgements

This study was supported by the National Key R&D Program of China (no. 2019YFA0606604, to W.L.). The authors also acknowledge funding from the ANR CLAND Convergence Institute (16-CONV-0003, to P.C.), the European Space Agency (ESA) within the CCI Biomass project (contract 4000123662/18/I-NB, to P.C.), the National Natural Science Foundation of China (grant no. 41971132 and grant no. U20A2090, to C.Y.), the Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDB40020305, to C.Y.) and the National Key Scientific and Technological Infrastructure project ‘Earth System Science Numerical Simulator Facility’ (EarthLab, to L.Y.). The global AGB dataset was generated as part of the European Space Agency (ESA) Data User Element (DUE) GlobBiomass project (ESRIN contract no. 4000113100/14/I-NB).

Author information

Authors and Affiliations

  1. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China

    Zhe Zhao, Wei Li, Le Yu, Lei Zhu & Jingmeng Wang

  2. Joint Center for Global Change Studies, Beijing, China

    Wei Li

  3. Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA CNRS UVSQ, Gif Sur Yvette, France

    Philippe Ciais & Hui Yang

  4. Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus

    Philippe Ciais

  5. Gamma Remote Sensing, Gümligen, Switzerland

    Maurizio Santoro & Oliver Cartus

  6. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Shushi Peng

  7. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA

    Yi Yin

  8. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, China

    Chao Yue

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Contributions

W.L. designed the research. Z.Z. performed analysis. M.S. and O.C. processed the biomass map data. Z.Z. and W.L. drafted the paper. All authors contributed to the interpretation of the results and to the text.

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Correspondence to Wei Li.

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The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Rico Fischer and other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Schematic of edge effects characterization.

Scale of the edge effects is the distance (km) where the biomass equals p % of the estimated biomass of interior forest (p=90 in this study). Magnitude is the difference (%) of biomass between interior forest and forest edge. We used d km away from the edge in practice to avoid negatives due to bad fittings. d=0.015 km is used in this study and d=0.232 km is used for the comparison with Chaplin-Kramer et al. (Supplementary Discussion 1).

Extended Data Fig. 2 Average tree cover of all forest pixels in each grid cell.

Shading in (c) indicates the interquartile ranges, and dashed lines indicate medians.

Source data

Extended Data Fig. 3 Frequency distributions of Δβ and β for different types of pixels across Africa.

a, Δβ is calculated as β at fire edge minus β at non-fire edge. b, β for different types of pixels (moist forest, dry forest, fire edge, non-fire edge). Shading in (a) indicates the weighted interquartile range, and the dashed line indicates the weighted median. Δβ > 0 was found in 66% of the grid cells. The order of β for different pixel types is: moist forest < dry forest < non-fire edge < fire edge.

Source data

Extended Data Fig. 4 Different scenarios of forest pixel (30 m) loss during the period 2010–2100.

Deforestation fraction between 2015 and 2100 for RCP2.6 and RCP8.5, derived from LUH2 v2f (0.25°), was used as an approximation to deforestation fraction between 2010 and 2100 for each grid cell. We only focused on grid cells with forest loss, and the gross forest loss area in Africa is the sum of the forest loss areas of these grid cells. We created four scenarios to match the deforestation fraction in each grid cell: (1) all pixels starting from current forest edges are deforested from the outer edge inwards until the target deforestation area is achieved, and thus new edges are created (S-1); (2) small forest patches are cleared first to minimize new forest edges (S-2); (3) pixels in the interior of forest are preferentially deforested (S-3); (4) pixels are deforested randomly, maximizing the area of new forest edges (S-4).

Extended Data Fig. 5 Local circulation that draws moisture from the forest edge zone to the edge.

The Schematic is adapted from Laurance et al. (2011)14. Edge effects result from local circulations that draw down moisture due to the gradient of β between forest edges and nearby forest patches. Fire-related edges have even higher β than non-fire edges, which enhances local circulations and edge effects. Two possible ways of fire influencing edge effects were defined: direct impact, i.e. direct burning of trees and understories, and indirect impact, i.e., fire-enhanced β at fire-edges compared to non-fire edges. The frequency distributions of β in the forest edge zone and at different edges are shown in Fig. E3.

Extended Data Fig. 6 Fraction of forest pixels with AGB > 250 Mg ha-1.

a-b, Frequency distributions of forest pixel (30 m × 30 m) AGB. Only 0.25° grids where edge effects were successfully detected were used. c-d, The fraction of forest pixels with AGB > 250 Mg ha-1 in each 0.25° grids where edge effects were successfully detected. Shading area in (a) and (b) indicates the interquartile ranges, and dashed lines indicate the medians.

Source data

Extended Data Fig. 7 AGB bias of the GlobBiomass map at different AGB levels.

Data of the solid line is from Supplementary Fig. S8 in Santoro et al. (2020)22, and data of the dots is based on field observations in the Amazon from Brienen et al. (2015)43. The field plot data in the Amazon were aggregated at each interval of 5 Mg ha-1, and the dots and error bars indicate means and standard deviations of GlobBiomass AGB in each 5 Mg ha-1 intervals.

Source data

Extended Data Fig. 8 Schematic of different types of forests and edges.

Hierarchical structure of pixels is described in Supplementary Methods. β values were averaged over fire- and non-fire edges separately in the same 0.25° grid cell, and Δβ (fire minus non-fire) was used as indicator of indirect fire impact on edge effects. The fire distance was used as an indicator of the direct impact of fires on edge effects. The median distance that fire burns into forests of forest pixels with fire edges (MF and DF) was used for each 0.25° grid cell.

Supplementary information

Supplementary Information

Supplementary methods, Discussions 1–3, Tables 1–3 and Figs. 1–7.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

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Source Data Fig. 3

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Source Data Extended Data Fig. 2

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Source Data Extended Data Fig. 3

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Source Data Extended Data Fig. 6

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Source Data Extended Data Fig. 7

Statistical source data.

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Zhao, Z., Li, W., Ciais, P. et al. Fire enhances forest degradation within forest edge zones in Africa. Nat. Geosci. 14, 479–483 (2021). https://doi.org/10.1038/s41561-021-00763-8

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