To estimate height for plots or trees a 3-parameter regional height-diameter Weibull equation was fitted using the local heights function in the BiomasaFP R package (Lopez-Gonzalez et al., 2015). We estimated the aboveground biomass in live stems, aboveground wood productivity, and AGB mortality, in Mg dry mass ha-1, at each census of each plot, and corrected for census-interval effects (Talbot et al., 2014).

## References

* ForestPlots.net et al. Taking the pulse of Earth's tropical forests using networks of highly distributed plots. 260, (2021).

* Phillips, O. L. et al. RAINFOR field manual for plot establishment and remeasurement. (2010)

* Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).

* Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).

* Qie, L. et al. Long-term carbon sink in Borneo's forests halted by drought and vulnerable to edge effects. Nature Communications 8, 1966 (2017).

* Lewis, S. L. et al. Above-ground biomass and structure of 260 African tropical forests. Philosophical Transactions of the Royal Society B: Biological Sciences 368, 20120295–20120295 (2013).

* Chave, J. et al. Towards a worldwide wood economics spectrum. Ecology Letters 12, 351–366 (2009).

* 39. Zanne, A. E. et al. Data from: Towards a worldwide wood economics spectrum. Dryad Digital Repository. (2009) doi:10.5061/dryad.234.

* Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. Journal of Vegetation Science 22, 610–613 (2011).

* Lewis, S. L. et al. Increasing carbon storage in intact African tropical forests. Nature 457, 1003–1006 (2009).

* Sullivan, M. J. P. et al. Field methods for sampling tree height for tropical forest biomass estimation. Methods in Ecology and Evolution 9, 1179–1189 (2018)

* Lopez-Gonzalez, G., Sullivan, M. J. P. & Baker, T. R. BiomasaFP: Tools for analysing data downloaded from ForestPlots.net. Version 0.3.0 (2015).

* Talbot, J. et al. Methods to estimate aboveground wood productivity from long-term forest inventory plots. Forest Ecology and Management 320, 30–38 (2014).

## Timing and spatial scale

The 123 plots have a median plot size of 1 ha, mean 1.05 ha (range 0.25 - 6.25 ha), with a total plot area of 128.7 ha. While some plots were established as early as 1962, to support temporal consistency among plots, and to avoid potential impacts of the 1982-1983 El Niño event, we include only censuses after 1983. Any additional censuses after the El Niño census were not used in this analysis. Plots were measured at least three times and a maximum of 27 times between 1984 and 2018. The plot monitoring periods span 3 to 32 years; mean initial census was May 2001, mean pre-El Niño census was September 2014, and mean post-El Niño census was May 2017. The mean pre-El Niño monitoring length was 13.3 years and the mean length of the El Niño interval was 2.7 years. See Table S1 for plot census details.

## Data exclusions

Plots were selected using the criteria described above (see Sampling strategy section). Plots that did not meet one or several of these criteria were not used for analysis.

## Reproducibility

Our analysis did not include experimental findings. All source data and code to rerun analysis and reproduce figures can be found at forestplots.net/data-packages/Bennett-et-al-2023.

## Randomization

No randomization occurred in our study.

## Blinding

Blinding was not relevant to our study.

Did the study involve field work?

<figure><img src="image_50.png" /></figure>

Yes

<figure><img src="image_51.png" /></figure>

No

## Field work, collection and transport

### Field conditions

All 123 plots are located in lowland (<1000 m), closed canopy, mature, South American tropical forests. The El Niño remeasurement campaigns took place between April 2016 and October 2018.

### Location

Plots are located between -23.40S to 11.20N latitude and 77.50W to 44.20W. A map showing locations of all plots is presented in Figure 1, and plot latitude and longitude is in Table S1.

### Access & import/export

Plot inventory El Niño remeasurement census data were collected by teams led by at least one of the researchers co-authoring this paper. This paper is a product of the RAINFOR and PPBio networks. These permanent inventory plot networks only exist because of huge community effort. A full list of people involved in data collection can be found in the Acknowledgments.

### Disturbance

No significant disturbance was caused by our measurements. Stems were tagged using a single aluminium nail (no iron), avoiding damage to trees due to corrosion.

## Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
To estimate height for plots or trees a 3-parameter regional height-diameter Weibull equation was fitted using the local heights function in the BiomasaFP R package (Lopez-Gonzalez et al., 2015). We estimated the aboveground biomass in live stems, aboveground wood productivity, and AGB mortality, in Mg dry mass ha-1, at each census of each plot, and corrected for census-interval effects (Talbot et al., 2014).

## References

* ForestPlots.net et al. Taking the pulse of Earth's tropical forests using networks of highly distributed plots. 260, (2021).

* Phillips, O.L. et al. RAINFOR field manual for plot establishment and remeasurement. (2010)

* Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).

* Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).

* Qie, L. et al. Long-term carbon sink in Borneo's forests halted by drought and vulnerable to edge effects. Nature Communications 8, 1966 (2017).

* Lewis, S. L. et al. Above-ground biomass and structure of 260 African tropical forests. Philosophical Transactions of the Royal Society B: Biological Sciences 368, 20120295–20120295 (2013).

* Chave, J. et al. Towards a worldwide wood economics spectrum. Ecology Letters 12, 351–366 (2009).

* 39. Zanne, A. E. et al. Data from: Towards a worldwide wood economics spectrum. Dryad Digital Repository. (2009) doi:10.5061/dryad.234.

* Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. Journal of Vegetation Science 22, 610–613 (2011).

* Lewis, S. L. et al. Increasing carbon storage in intact African tropical forests. Nature 457, 1003–1006 (2009).

* Sullivan, M. J. P. et al. Field methods for sampling tree height for tropical forest biomass estimation. Methods in Ecology and Evolution 9, 1179–1189 (2018).

* Lopez-Gonzalez, G., Sullivan, M. J. P. & Baker, T. R. BiomasaFP: Tools for analysing data downloaded from ForestPlots.net. Version 0.3.0 (2015).

* Talbot, J. et al. Methods to estimate aboveground wood productivity from long-term forest inventory plots. Forest Ecology and Management 320, 30–38 (2014).

## Timing and spatial scale

The 123 plots have a median plot size of 1 ha, mean 1.05 ha (range 0.25 - 6.25 ha), with a total plot area of 128.7 ha. While some plots were established as early as 1962, to support temporal consistency among plots, and to avoid potential impacts of the 1982-1983 El Niño event, we include only censuses after 1983. Any additional censuses after the El Niño census were not used in this analysis. Plots were measured at least three times and a maximum of 27 times between 1984 and 2018. The plot monitoring periods span 3 to 32 years; mean initial census was May 2001, mean pre-El Niño census was September 2014, and mean post-El Niño census was May 2017. The mean pre-El Niño monitoring length was 13.3 years and the mean length of the El Niño interval was 2.7 years. See Table S1 for plot census details.

## Data exclusions

Plots were selected using the criteria described above (see Sampling strategy section). Plots that did not meet one or several of these criteria were not used for analysis.

## Reproducibility

Our analysis did not include experimental findings. All source data and code to rerun analysis and reproduce figures can be found at forestplots.net/data-packages/Bennett-et-al-2023.

## Randomization

No randomization occurred in our study.

## Blinding

Blinding was not relevant to our study.

Did the study involve field work?

<figure><img src="image_50.png" /></figure>

Yes

<figure><img src="image_51.png" /></figure>

No

## Field work, collection and transport

### Field conditions

All 123 plots are located in lowland (<1000 m), closed canopy, mature, South American tropical forests. The El Niño remeasurement campaigns took place between April 2016 and October 2018.

### Location

Plots are located between -23.40S to 11.20N latitude and 77.50W to 44.20W. A map showing locations of all plots is presented in Figure 1, and plot latitude and longitude is in Table S1.

### Access & import/export

Plot inventory El Niño remeasurement census data were collected by teams led by at least one of the researchers co-authoring this paper. This paper is a product of the RAINFOR and PPBio networks. These permanent inventory plot networks only exist because of huge community effort. A full list of people involved in data collection can be found in the Acknowledgments.

### Disturbance

No significant disturbance was caused by our measurements. Stems were tagged using a single aluminium nail (no iron), avoiding damage to trees due to corrosion.

## Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>☐ Antibodies</td></tr><tr><td>☑</td><td>☐ Eukaryotic cell lines</td></tr><tr><td>☑</td><td>☐ Palaeontology and archaeology</td></tr><tr><td>☑</td><td>☐ Animals and other organisms</td></tr><tr><td>☑</td><td>☐ Human research participants</td></tr><tr><td>☑</td><td>☐ Clinical data</td></tr><tr><td>☑</td><td>☐ Dual use research of concern</td></tr></tbody></table>

Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>☐ ChIP-seq</td></tr><tr><td>☑</td><td>☐ Flow cytometry</td></tr><tr><td>☑</td><td>☐ MRI-based neuroimaging</td></tr></tbody></table>
Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>☐ Antibodies</td></tr><tr><td>☑</td><td>☐ Eukaryotic cell lines</td></tr><tr><td>☑</td><td>☐ Palaeontology and archaeology</td></tr><tr><td>☑</td><td>☐ Animals and other organisms</td></tr><tr><td>☑</td><td>☐ Human research participants</td></tr><tr><td>☑</td><td>☐ Clinical data</td></tr><tr><td>☑</td><td>☐ Dual use research of concern</td></tr></tbody></table>

Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>☐ ChIP-seq</td></tr><tr><td>☑</td><td>☐ Flow cytometry</td></tr><tr><td>☑</td><td>☐ MRI-based neuroimaging</td></tr></tbody></table>