Researchers have found that interactions causing self-amplified forest loss in the Amazon is often connected to extreme drought events. Photo: H. Nurhatmadi/iStock

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forest loss in the amazon

Breaking a vicious cycle

Researchers identify connections between extreme drought and self-amplified forest loss. Forest diversity reduces risk of such loss

Story highlights

  • Few analyses have looked into the interactions causing self-amplified forest loss in the Amazon
  • The researchers found that the mechanisms responsible for this kind of loss seem to occur during extreme drought events
  • Building resilience by maintaining forest diversity is thought to be one way to break forest loss cycles before they get too far

The Amazon forest, one of the world’s most iconic ecosystems may be at risk of a massive vegetation shifts through so-called self-amplified forest loss, says an international team of researchers in a study in Nature Communications.

The Amazon has been under immense stress from climate change. This is amplified in combination with deforestation, logging and fire.

Many models have made progress in trying to map vegetation-climate interactions, but few analyses have looked into the interactions causing self-amplified forest loss. In a recent paper PhD student Lan Wang-Erlandsson contributed to a paper on self-amplified forest loss in the Amazon. The study was led by Dr. Delphine Clara Zemp who is affiliated with Humboldt Universität zu Berlin and Potsdam Institute for Climate Impact Research.

Understanding the mechanisms of self-amplified forest loss is also key to understand the forces that stabilise the Amazon

Delphine Clara Zemp, lead author

The pressures of extreme drought

The researchers found that the mechanisms responsible for this kind of loss seem to occur during extreme drought events. Wang-Erlandsson explains, “more frequent and severe future drought events may push the Amazon towards large-scale self-amplified forest loss, without drastic reductions in long-term average rainfall.”

During the Last Glacial Maximum 21,000 years ago, decline in oceanic moisture caused a 50 % reduction in the Amazon dry season rainfall. In such dry season conditions, 10-13 % of forest dieback in the Amazon basin could be attributed to self-amplified forest loss. On the higher end, as much as 18-23 % of the Amazon forest could be affected. With 40 % reduced dry-season rainfall, as in severe end of the 21st century projections, 1-7 % (potentially up to 14 %) of Amazon forest loss could be attributed to self-amplifying effects.

Figure shows vegetation-atmosphere feedbacks resulting ins self-amplified forest loss. This is the starting point to identify the sensitivity of the Amazon vegetation to these feedbacks and what climate conditions might trigger them. Illustration: Zemp et al. 2017

Differences across the forest

Like many complex adaptive systems, the trees in the Amazon forest are part of a larger network. In their analysis, the researchers found that forest loss occurs non-linearly. This means that forest shifts in one part of the Amazon might trigger changes in other areas of the basin in ways that can be difficult to predict.

For instance, changes in oceanic moisture lead to initial forest shifts in the south-eastern part of the Amazon basin and trigger self-amplified forest loss in regions located further south and west.

There is hope though. Through their modelling approach, the researchers found that rainforests with lower resilience are more likely to shift to a savanna as a response to stressors, like extreme drought and fire. Building resilience by maintaining diversity is thought to be one way to break forest loss cycles before they get too far.

The right heterogeneity

Previous modelling studies have shown that spatial heterogeneity is important for the stability of ecosystems and complex networks in general. The question is what kind of heterogeneity is important in the Amazon? What they found was that larger heterogeneity in forest resilience thresholds reduces the frequency of high-order cascades by more than 50%.

For example, if individual forest patches shift at different times in response to varying amounts of rainfall, then the propagation of forest loss is usually stopped at early stages of the cascade. Variability alleviates the risk of long-term self-amplified forest loss.

While this study looked at the sensitivity of these feedbacks, it does not resolve underlying processes of forest dieback. Understanding climate conditions that are more likely to cause forest loss than others are valuable for forest management.

As the researchers conclude, "our results show how important forest diversity is for reducing risk of self-amplified forest loss. This is an important take-home message for conservation management and policy."

Methodology

The researchers use a reconstruction of moisture recycling networks obtained from atmospheric moisture tracking from climate data. Nodes in this network represent vegetation that are linked together by monthly water fluxes and represent real moisture recycling processes. This network is combined with empirical indicators of forest resilience to identify non-linear regional vegetation-atmospheric interactions.

They use input data for the period 1989-2005 for tropical South America from multiple sources, including: Climate Research Unit (CRU), the Global Precipitation Climatology Centre (GPCC), the Global Precipitation Climatology Project (GPCP), the unified climate prediction center (CPC) and the National Oceanic and Atmospheric Administration (NOAA).

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Related info

Zemp, D.C., Schleussner C-F., Barbosa, H. M. J., Hirota M., Montade V., Sampaio G., Staal A., Wang-Erlandsson L., and Rammig A. 2017. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8, 14681. DOI: 10.1038/NCOMMS14681

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Lan Wang-Erlandsson is a PhD student who looks at how large-scale modifications in land-use and evaporation may affect downwind precipitation through the process known as moisture recycling

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