Figure 1. Map of Brazil identifying the Caatinga biome from where the
data of 1141 municipalities were used to understand the relationship
between forest and food security.
We built a multidimensional food
security index for two time-points (2006 and 2017) to understand how it
changed over time and how it is related to forest cover change. We built
one index using a Principal Component Analysis for each year to reduce a
large number of variables into a few dimensions following a method
proposed by Hummel et al(2016). We selected
only the PCs with an eigenvalue greater than 1
(Cutter et al.,
2003) and then we changed the cardinality of each PC depending on
whether the variables that compose the PC contribute positively or
negatively to food security (Supplementary Table 2). The final food
security index for each year was calculated as the sum of the individual
scores of each PC for each municipality based on Hummel et al(2016). We estimated
the food security change in the municipalities by calculating the
difference between the final index score between 2006 and 2017.
We gathered the data of native
vegetation cover from the MapBiomas project (MapBiomas, 2020). We
considered all categories of forest (forest, savanna, mangrove and
forest plantations) and non-forested native vegetation (wetland,
grassland, salt flats, rocky outcrops and other non-forest formations)
to calculate the forest cover percentage for each municipality. We
grouped all types of native vegetation into one class (i.e., native
vegetation cover) because people in the Caatinga derive many uses
related to food security from all types of native vegetation and not
only from forests
(Albuquerque et
al., 2017). Then, we identified and spatialized the municipalities
with synergies (win-win and lose-lose) and trade-offs (win-lose and
lose-win) between forest cover and food security change, respectively.
We considered all municipalities that gained food security from 2006 to
2017 as a ‘win’ group, irrespective of the scores’ size, as well as
‘lose’ if the food security score was below zero.
Statistical analyses
We first principal components
analyses to reduce dimensionality of our Multidimensional Food Security
Index (MFSI). We used z-transformation to standardise scores of the
principal components and calculate the MSFI. Because we changed the
cardinality of the dimensions to always increase MFSI, the index is the
sum of both positive (increasing security) and negative values
(decreasing security). Because the dimensions were formed by different
variables across years we provided a table to help readers interpret
what drives food insecurity (Table S1). The absolute changes of values
of MFSI and forest cover were used to create maps that help to
understand spatial distribution of both changes in forest cover and food
security. To understand the relationship between forest cover and MFSI
and its two most important principal components in 2006 and 2017 we
built spatial regression models with a quadratic term of forest cover
when testing the effect of forest cover on MFSI and its first principal
component (PC-1) and without quadratic term for the second PC. In all
models, spatial error was included to check for nonlinear relationships.
We used the errorsalm function from the package ‘spdep’ in the R
environment. Moran I test, using the function moran.test was then
used to test whether after accounting for spatial error, there was still
spatial autocorrelation of the residuals.
Results