Climate change is negatively affecting tropical regions through increasing temperatures and decreased precipitation leading to changes in local hydrology and decreasing water supply among others. In order to make accurate future predictions of carbon stock and forest health it is necessary to better understand the current underlying baseline carbon stock and how it may vary across space. Here we adapted an existing carbon stock assessment method and applied it to two tropical regions in Nicaragua and Costa Rica managed by the Maderas Rainforest Conservancy. Carbon stock was calculated based on 1) above-ground tree biomass, 2) above-ground sapling biomass, 3) leaf litter, herb and grass biomass, 4) soil organic carbon, 5) below-ground biomass, 6) stumps and deadwood and 7) regenerating plants. Our results show a strata-pooled average of 234.09 ± 379 Mg C ha-1 (n=40) carbon at the Costa Rican site and 209.20 ± 216 Mg C ha-1 (n=40) at the Nicaraguan site. These values are much higher than those available on a biome-wide scale, highlighting the extent of carbon stock loss outside these study areas as a result of anthropogenic disturbances, in comparison to more pristine areas. Local investigations into carbon stocks in the tropics are necessary to better estimate the current state of carbon content in the tropics. By adapting existing sampling protocols to local conditions this can be achieved efficiently. Furthermore, local estimates of carbon stock enable non-governmental organizations (NGOs) to participate in the Reducing Emissions from Deforestation and forest Degradation (REDD) program led by the United Nations.


1 2 3
November 23rd, 2016

#ggforce for accelerating #ggplot2 in #dataviz

November 20th, 2016

November 16th, 2016

ggplot2 2.2.0 #dataviz #R #datascience @rstudio

November 16th, 2016

ggedit add-on for #ggplot2 #dataviz #R #datascience

August 27th, 2016

#R Packages for #Data Access

August 16th, 2016

Getting Your Colleagues Hooked on #R

August 14th, 2016

Convolutional #neuralnetwork in #R (MXNet package) #MachineLearning #DataScience

June 3rd, 2016

Mad Hatter Explains Support Vector Machines #scicomm #machinelearning #SVM #datascience

May 11th, 2016

What’s the difference between machine learning, statistics, and data mining?

April 6th, 2016

Plotter app for interactive plotting of ggplots, on or locally.