Data from: Lianas associated with continued forest biomass losses following large-scale disturbances
Data files
May 28, 2024 version files 494.73 KB
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LTR.csv
1.61 KB
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README.md
4.41 KB
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RF_models2011.csv
242.16 KB
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RF_models2019.csv
242.46 KB
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RF_predictors.csv
4.08 KB
Abstract
Lianas are important to rainforest ecosystems but often impede tree growth and increase tree mortality and stem damage after disturbances that favour their growth. Understanding how lianas affect biomass recovery and rates of carbon sequestration following disturbance is therefore of crucial importance. In this study, we determine how a tropical forest recovers biomass following a large-scale disturbance and test how this varies with liana dominance and stem damage. We use remote sensing methods to develop a model, validated by field data from 40 20 m x 20 m vegetation plots, to measure change in tree above-ground biomass eight years after Tropical Cyclone Yasi damaged logged forests in the Australian Wet Tropics. We related tree biomass changes to field measures of current liana dominance over trees, expressed as liana: tree basal area ratio, and assessed how these measures related to tree stem damage. Biomass declined in 34 of the 40 plots during the eight years post-disturbance, with loss rates and proportions of damaged tree stems increasing with the liana: tree ratio. From spatial upscaling, we found a net loss in biomass across the study landscape over the same period. Our results show that, following disturbances, lianas not only limit tree biomass recovery but are also associated with further biomass declines, most likely through their contribution to stem damage and delayed mortality. Furthermore, our finding of net biomass loss across the landscape since the cyclone shows that, post-disturbance, rainforests can act as a carbon source with consequences for the global carbon sink.
https://doi.org/10.5061/dryad.prr4xgxvh
We measured tree biomass across a plot network of 40 20-m x 20-m vegetation plots in the rainforests of the Australian Wet Tropics. Field-measured biomass and remotely sensed predictors derived from RapidEye Surface Reflectance imagery were used to create a predictive biomass model that could be: i) upscaled to predict biomass across the broader study landscape, and ii) applied to historic remote sensing data to predict biomass in the past, and therefore measure biomass change through time. Remotely sensed predictors included textural metrics of the red-edge and NIR spectral bands and elevation extracted from a DEM. Biomass changes at the plot-level were related to field-measured liana: tree ratio (liana basal area relative to tree basal area) and the proportion of broken tree stems.
This dataset contains the output of a random forest model trained to predict biomass at plot-level using predictors derived from remote sensing with 95% confidence intervals calculated around each prediction. It also contains information on the remotely sensed predictors, liana: tree ratios and broken stems.
Description of the data and file structure
‘RF_predictors’ contains the values for each remotely sensed predictor that corresponds to each plot.
‘plot_num’ refers to the plot number
‘plot_name’ refers to the plot’s assigned name.
‘disturbance’ refers to whether the plot was categorised as heavily (<25% canopy cover of trees <10m) or lightly disturbed (>75% canopy cover of trees >10m)
‘B4_CON3.2011’ refers to the contrast of the red-edge band at a moving window size of 3 x 3 pixels from the 2011 RapidEye imagery.
‘B4_VAR9.2011’ refers to the variance of the red-edge band at a moving window size of 9 x 9 pixels from the 2011 RapidEye imagery.
‘B5_CON3.2011’ refers to the contrast of the NIR band at a moving window size of 3 x 3 pixels from the 2011 RapidEye imagery.
‘dem’ refers to elevation derived from the Digital Elevation Model.
‘B4_CON3.2019’ refers to the contrast of the red-edge band at a moving window size of 3 x 3 pixels from the 2019 RapidEye imagery.
‘B4_VAR9.2019’ refers to the variance of the red-edge band at a moving window size of 9 x 9 pixels from the 2019 RapidEye imagery.
‘B5_CON3.2019’ refers to the contrast of the NIR band at a moving window size of 3 x 3 pixels from the 2019 RapidEye imagery.
The file ‘RF_models2019’ contains the random forest predictions based on remotely sensed data from 2019. Each column V1-V500 holds the prediction from each of the 500 random forest trees. The column ‘mean.rf’ holds the mean of these predictions (the final prediction) and ‘upr’ shows the 97.5% upper confidence interval around this mean and ‘lwr’ shows the 2.5% confidence interval around this mean.
The ‘RF_models2011’ contains the predictions based on remotely sensed data from 2011. Each column V1-V500 holds the prediction from each of the 500 random forest trees. The column ‘mean.rf’ holds the mean of these predictions (the final prediction) and ‘upr’ shows the 97.5% upper confidence interval around this mean and ‘lwr’ shows the 2.5% confidence interval around this mean.
The file ‘LTR’ contains the plot-level data for liana: tree ratio and proportion of broken stems.
‘plot_num’ refers to the plot number
‘plot_name’ refers to the plot’s assigned name.
‘disturbance’ refers to whether the plot was categorised as heavily (<25% canopy cover of trees <10m) or lightly disturbed (>75% canopy cover of trees >10m)’LTR’
‘LTR’ is the ratio of lianas to trees, calculated as plot-level liana basal area / tree basal area.
‘broken. stem’ is the proportion of damaged stems (calculated as the plot-level total number of broken tree stems / total number of tree stems)
Sharing/Access information
Data was derived from the following sources:
- The RapidEye Surface Reflectance spectral data was downloaded from https://api.planet.com.
- The Digital Elevation Model was downloaded from the Queensland Spatial Data Catalogue, available online at https://qldspatial.information.qld.gov.au/catalogue/custom/index.page.